Hydroponic Agriculture: Cultivating the Future of Sustainable Farming

Interestingly, suppression of endodermal ABA signalling seems to contribute to the inactivation of aquaporin-mediated Lpr in a wild-type Scheduling low but frequent NO3 − applications, at-tuned to crop demand, allows the crop to take up most of the NO3 − before it passes through the low-salinity zone into the saline fringes. Figure 7 simulates continuous NO3 − application and a scenario which applies NO3 − only every 10 d, while the total amount of NO3 − applied is the same for both simulations. High-frequency applications of NO3 − using drip irrigation in-creased N uptake efficiency in some cases .Both Casparian strips and suberin lamellae, two extracellular hydrophobic barriers located in the wall of endodermal cells of the root, are thought to play important roles in restricting the free diffusion of solutes and water . Casparian strips act as apoplastic barriers not only to block solutes moving into the xylem through the free space between cells, but also to prevent their backfow from the stele to the apoplast of the cor-tex. Suberin lamellae, due to their deposition between the endodermal plasma membrane and secondary cell wall, do not block aploplastic transport but rather limit transcellular transport of nutrients and possibly water at the endodermis. Cross talk between the Casparian strip and suberin lamellae exists, with suberin being deposited in response to disruption of Casparian strips . Tese extracellular barriers are therefore at a cross-road between control of mineral nutrient and water uptake. However, the mechanisms that allow plants to integrate both these barrier functions to enable the simultaneous uptake of sufcient water and mineral nutrients remain under explored. Te dirigent-like protein Enhanced Suberin1 functions in the correct formation of Casparian strips by allowing the lignin, deposited at the Casparian Strip Domain through the action of Peroxidase64 and the Respiratory Burst Oxidase Homolog F ,blueberry packaging to form into a continuous ring. In the absence of this dirigent-like protein defective Casparian strips are formed along with enhanced and early deposition of suberin in the endodermis.

A similar pattern of Casparian strip disruption and response is also observed when the Casparian Strip Domain is disrupted through the loss of Casparian Strip Domain Proteins. Tese changes lead to systematic alterations in the profile of mineral nutrients and trace elements accumulating in leaves, and this phenotype provided the first tool for identification of genes involved in Casparian strip development. Detection of the diffusible vasculature-derived peptides CASPARIAN STRIP INTEGRITY FACTORS 1 & 2 through interaction with the SCHENGEN3 receptor-like kinase is what drives this endodermal response to loss of Casparian strip integrity. Here, we report that detection of a loss of Casparian strip integrity at the root endodermis by the CIFs/SGN3 pathway leads to an integrated local and long-distance response. This response rebalances water and mineral nutrient uptake, compensating for breakage of the Casparian strip apoplastic seal between the stele and the cor-tex. This rebalancing involves both a reduction in root hydraulic conductivity driven by deactivation of aquapor-ins, and limitation of ion leakage through deposition of suberin in endodermal cell walls. This local root-based response is also coupled to a reduction in water demand in the shoot driven by ABA-mediated stomatal closure.Te dirigent-like protein Enhanced Suberin1 functions in the formation of Casparian strips by allowing the correct deposition of lignin at the Casparian strip domain. Te enhanced deposition of suberin in the esb1-1 mutant with disrupted Casparian strips can clearly be observed using the lipophilic stain Fluorol Yellow 088 close to the root tip , and this can be quantified by counting the number of endodermal cells afer the onset of cell expansion to the first appearance of yellow fuorescence . This early deposition of suberin is also verifed by the clear correspondence of FY 088 staining with enhanced promoter activity of known suberin biosynthetic genes, including GPAT5 monitored through both GUS staining and GFP fuorescence , and also others through GUS staining . This is further reinforced by the enhanced expression of known suberin biosynthetic genes in esb1-1 relative to wild-type . To better understand the causal link between Casparian strip integrity and enhanced deposition of suberin, we performed a reciprocal grafing experiment that revealed that the esb1-1 mutation is only required in the root to drive enhanced deposition of suberin at the endodermis, placing the function of ESB1 and the driver for increased suberin in the same tissue .

To determine the cause and effect rela-tionship between damaged Casparian strips and enhanced suberin we carefully monitored the first appearance of both Casparian strips and enhanced suberin in esb1-1. Using lignin staining in the Casparian strip marker line pCASP1::CASP1::GFP, we are able to observe that damaged Casparian strips are visible 2.5 days afer sowing . This is at least 12hr before the first indication of enhanced suberin biosynthesis, which we monitor using promoter activity of suberin biosynthetic genes GPAT5, FAR4, FAR1 and FAR5 . This was also verified by the direct observation of suberin deposition with FY 088 . Te observation that treatment with the CIF2 peptide, normally leaked from the stele through loss of Casparian strip integrity, can enhance suberin deposition in wild-type plants supports our interpretation that enhanced suberin deposition is a response to loss of integrity of the Casparian strip-based apoplastic diffusion barrier. Furthermore, loss-of-function of the receptor-like kinase SGN3, required for sensing of CIFs, blocks the enhanced deposition of suberin in esb1-1 and casp1-1casp3-1 based on a chemical analysis of suberin in esb1-1 , and also on FY 088 staining. We conclude that Casparian strip defects sensed by the CIFs/SGN3 surveillance system lead to enhanced deposition of suberin in the endodermis.Te observation that enhanced suberin is deposited as a response to loss of integrity of the endodermal-based diffusion barrier between stele and cortex, raises the question, what is the function of this increased suberin deposition? Previously, the extent of endodermal suberin has been shown to be part of the response to nutrient status . We therefore tested the selectivity to solutes σNaCl, in roots varying in the extent of suberin deposition and the functionality of Casparian strips. For this, we measured solute leakage into xylem sap of pressurized roots at increasing sodium chloride concentrations in the solution bathing the roots. Taken individually, σNaCl of roots of esb1-1, sgn3-3 and wild-type were not significantly different from one another , which is surprising given the disruption of the Casparian strip-based apoplastic diffusion barrier in both mutants.

However, removal of suberin in esb1-1, by endodermal-specific ectopic expression of a cutinase ,blueberry packaging box caused a significant decrease in σNaCl compared to wild-type plants , and a similar tendency when compared to esb1-1 . This supports the notion that enhanced suberin deposition at the endodermis helps prevent passive solute leakage caused by defects in the Casparian strips of the esb1-1 mutant. We also observed a significant decrease in σNaCl in the double mutant esb1-1sgn3-3 compared to both wild-type and sgn3-3 . It is known that SGN3 is required for the enhanced deposition of suberin that occurs at the endodermis in esb1-1 . Our observation that removal of this enhanced suberin in esb1-1sgn3-3 decreases σNaCl further supports our conclusion that the role of this increased suberin deposition is to limit solute leakage where Casparian strip barriers are disrupted.It has also been suggested that endodermal suberin may impact water permeability, though how is still unclear. To further address the role of enhanced endodermal suberin, we investigated root hydraulic conductivity of esb1-1 and observed a significant reduction by 62% with respect to wild-type . Importantly, this difference in esb1-1 Lpr originates mainly from a reduction in an aquaporin-mediated water transport pathway . We also observed that the azide-resistant water transport pathway was lower in esb1-1 than in wild-type , yet to a lesser extent than the aquaporin mediated pathway. Te dra-matic reduction in aquaporin-mediated Lpr in esb1-1 we observe is an intriguing fnding, which led us to consider if this lack of aquaporin activity in esb1-1 roots is due to a direct output from the CIFs/SGN3 signalling pathway, or if it represents an efect downstream of enhanced suberin deposition. We found that removal of endodermal suberin in esb1-1 through expression of CDEF1 in the endodermis had no further efect on Lpr . This rules out a role for suberin in the reduced aquaporin-mediated Lpr of esb1-1. However, in the esb1-1sgn3-3 dou-ble mutant, as compared to esb1-1, we observed a full recovery of Lpr back to wild-type levels . Loss of Casparian strip integrity in esb1-1 therefore appears to be sensed by the CIFs/SGN3 signalling pathway, which leads to the inactivation of aquaporins, thereby reducing Lpr . To support this conclusion, we show that exogenous application of CIF2 to wild-type plants for 3h induces a reduction in Lpr, and only in the presence of a functional SGN3 . We have established the existence of two critical outputs of the CIFs/SGN3 diffusion-barrier surveillance system. Tese are enhanced deposition of endodermal suberin limiting solute leakage, and the inactivation of root aquaporin activity reducing Lpr. Do these two independent outputs of the CIFs/SGN3 diffusion barrier surveillance system work in parallel, or in series with one response leading to the other? Te fact that removal of endodermal suberin in esb1-1 does not compensate for its reduced Lpr suggests that enhanced suberin deposition is not the cause of the reduced aquaporin-mediated Lpr. However, reduced activity of aquaporins through loss-of-function of the two major aquaporins PIP2;1 and PIP2;2 in the pip2;1pip2;2 double mutant, does cause significant increases in endodermal suberin deposition . A similar increase in suberin is also observed afer treatment with the aquaporin inhibitor sodium azide through observation of the activity of the transcriptional reporter pGPAT5::mCITRINE-SYP122 for suberin biosynthesis. GPAT5 expression is observed to expand toward the root tip after 6 hours only of sodium azide treatment .

Based on this evidence, we propose the following sequence of events. Casparian strip defects are detected by the apoplastic leakage of CIFs from the stele, being sensed by SGN3. Once activated, SGN3 signals the inactivation of aqua-porins thereby reducing Lpr which in turn leads to the early and enhanced deposition of endodermal suberin. Insuch a model, SGN3 would inhibit aquaporin function, which may appear at variance with the usual activation of aquaporins through phosphorylation. Yet, such an inhibition was recently described in the case of FERONIA, a protein kinase inactivating PIP2; 1 function through an as yet unknown mechanism.Abscisic acid has been shown to be involved in regulating both aquaporin activity reviewed in and suberin deposition, making ABA an interesting can-didate worth exploring for a role in downstream CIFs/SGN3 signalling. To probe this possibility we expressed the dominant negative allele of the regulator of ABA signalling ABA-INSENSITIVE 1 in the endoder-mis of esb1-1 using pELTP::abi1. This abi1 construct specifically blocks ABA signalling at the endodermis and delays suberisation in a wild-type background as previously shown in . In esb1-1, we observed abi1 to have no effect on either the inactivation of aquaporins or the enhanced deposition suberin . We also observe that aquaporin inhibition with sodium azide in the pELTP::abi1-1 line still induces expression of the suberin biosynthesis gene GPAT5 toward the root tip in the pGPAT::mCITRINE-SYP122 line, as observed in wild-type . Based on this, activation of ABA signalling in the endodermis does not link perception of Casparian strip defects with the downstream responses of reduced aquaporin-mediated Lpr or suberin deposition. Suppbackground .The esb1-1 mutant is known to have reduced stomatal apertures and enhanced wilting resistance. This suggests that the CIFs/SGN3 sensing system not only initiates a local root response to Casparian strip integrity but is also involved in initiating long-distance responses in the shoot. We observe reduced stomatal apertures in esb1-1 , and an analysis of the expression of a set of known ABA signalling and response genes in leaves suggest that this stomatal closure is part of an ABA driven response. The aba1 mutation confers a strong ABA deficiency.By generating an esb1-1aba1 double mutant, we investigated the ABA-dependent component in the leaf response we observe in esb1-1. ABA-defciency in esb1-1aba1 suppressed both the reduced stomatal aperture and the activation of expression of ABA signalling and response genes that we observe in esb1-1 .

Existing trait-based models that predict abundances of relevant taxa could serve as a useful starting point

Farmers do not manage for traits directly, but rather manage agroecosystems by manipulating the abundances and location of species or through physical and chemical manipulation of the agroecosystem . Traits are used implicitly by selecting or promoting species that have certain functional properties . Yet, management targets based on functional traits offer an opportunity to create management strategies tailored to environmental conditions and biotic interactions when the relation between species, their traits, and the environment is well understood. Given that farmers manipulate species and their abiotic environment, effective management strategies require an understanding of how trait response to the environment can be translated to the relative abundance targets for species. Farmers could then manipulate the biological, physical, or chemical components of agroecosystems to achieve these abundance targets. Management targets could be generated through quantitative trait-based modeling that converts functional trait based objectives into targets for the relative abundances of species .In this way, data on functional traits of a local species pool could be used to determine the relative abundance of species needed to achieve a functional traitgoal. A management strategy could then be implemented to try to achieve this relative abundance and then to test whether the implemented community meets the established functional trait goals and the delivery of the desired ecosystem services. For planned diversity, establishing communities with certain relative abundances is relatively straightforward . Storkey et al. used a model of plant competition to identify the community of 12 different cultivated legume species that delivered the greatest value of multiple ecosystem services. Low to medium levels of species diversity that captured wide functional contrasts were identified as being optimal. For associated diversity, which depends on ecological processes embedded in an agricultural setting, establishing and maintaining communities requires understanding how species, and their traits,ebb and flow tray respond to the specific management practices used; for example, how response traits determine the response of pollinator abundances to the presence of certain types of planted vegetation.

Several approaches have been proposed, for example, to increase the abundance of pest enemies, including habitat modification and food supplementation. However, it has been difficult to empirically assess how these factors contribute to the balance of natural enemies and pests and, thus, the level of pest control and resulting differences in crop yields. Given the importance of space and trophic position in determining agroecosystem services, trait-model iterations of management targets ought to be applied to specific spatial and trophic scales. Given that the implementation of these targets is iterative , it will be important to also consider how the properties of species and ecosystems change over the course of implementation .The problem of sample selection arises frequently in agricultural economics, such as in studies of individuals’ wages or labor supply. With large data sets of “well-behaved” data, the traditional approaches perform well These models include the two-step and maximum likelihood sample-selection approaches as well as the semi-parametric class of estimators . However, when sample sizes are small, data are non-experimental and somewhat contaminated,’ perhaps due to multicollinearity, and the researcher is not sure what data-generation process underlies the data, the traditional models may have difficulties and may produce unstable results. Unfortunately, many if not most data sets have these limitations and therefore traditional methods may not be fully satisfactory. Our objective is to summarize a new, semi-parametric approach for estimating small data set sample-selection problems and use it to examine an important problem in agricultural labor economics. The approach we take grew out of information theory and is based on the classical maximum entropy approach and the generalized maximum entropy work of Galan, Judge and Miller. Our main goal is to estimate the set of unknown parameters, incorporating all the possible information in the estimation procedure without making apriori assumptions regarding the underlying distribution. We use our method to study how agricultural employees choose to work in piece-rate or time-rate sectors, how the wage equations differ across these sectors, and how the female male wage differential varies across regions. Because we are interested in regions, the sample sizes are relatively small and traditional approaches may not perform well. We compare our estimates to those of four other methods. The first section specifies the sample-selection model. Section 2 develops the background and discusses the GME estimation model. Section 3 lists the relevant inference and diagnostic measures. Section 4 discusses the data and the main empirical results.Section 5 contains conclusions.

We want to examine how individuals decide whether to work in the piece-rate or time rate sectors of the agricultural labor market, whether women are paid less then men in these sectors, and whether these earnings differentials vary geographically. Consequently, we estimate the same model for various regions of the country. In these models, wage depends on the X matrix which includes age and age squared; farm work experience and its square; and dummies for white , females, and legal status . The C matrix includes these variables and whether the individual can speak English. For the Western Plains region, we drop the amnesty dummy due to lack of variation and include a dummy for Texas. We do not estimate the model for the North West region due to the lack of variation in many variables. We estimated models of piece-rate and time-rate wage equations and selection equations for each region using the GME and four other models: ordinary least squares , Heckman’s two-step estimator, Heckman’s fuIl-information maximum likelihood estimator, and Ahn-Powell’s method. The consistency of both of Heckman’s estimators depend on the assumption of joint normality of the residuals, which may be violated in our samples. Neither Heckman model produces fully acceptable estimates for any region. In the following tables, we do not report estimates for Heckman’s maximum likelihood estimator because it either falls to converge or its estimated ‘correlation coefficient lies outside the [-I, I] range for every region. We do list estimates for the Heckman two-step procedure even though the correlation between residuals of the selection equation and the wage in at least one sector lies outside [-I, I], for each sector. Where such a violation occurs, we report a “constrained” correlation coefficient of -1.The AP model uses a two-step estimator where both the joint distribution of the error term and the functional form of the selection equation is unknown. Because the AP estimator is robust to mis specification of the distribution of residuals and the form of the selection equation, we expect the AP estimator to perform better than Heckman’s parametric two-step estimator for large samples. Whether the AP method has an advantage in small samples is not clear. 

Table I reports estimates of the wage coefficients for the Mid West. Though the general sign patterns are similar across the models, the GME coefficients tend to have much smaller asymptotic standard errors than the other estimates – especially in the piece-rate sector, which has few observations. The coefficient patterns are generally similar to those found in the literature , but less precisely rneasured by the Heckman estimators, presurnably because the earlier studies used larger samples than here. For all models that we can, we calculate the R2 goodness of fit measure for both wage equations using the same method as for ordinary least squares. The AP model does not have a goodness of fit statistic as it does not estimate constants.The Heckman does slightly better at predicting the time-rate sector, but the GME does better in predicting the piece-rate sector. The GME does better overall, correctly predicting 92.5 percent compared to 86.8 percent for the two-step method. Results are sirnilar in other regions. For example, in the Western Plains region , the Heckman model predicts 79.2 percent of the observations accurately, while GME predicts 98.7 percent correctly. The corresponding percentages are 69.5 percent and 93.4 percent for the South East and 93.5 percent and 100 percent for California . For ease in comparing the various models, the Heckman sample-selection prabit equation contains the same variables as in the C matrix, which we use in the GME model to estimate the relative cost of being in the time-rate sector in each of the inequality restrictions . However, one right argue that only the constant term and the “extra” variable – the ability to speak English – belongs in the C matrix. The entropy-ratio test that the other nine coefficients are zero is 0.02, which is smaller than the critical value of X~ using a 0.05 criterion. Thus, we conclude that these other nine variables do not contain statistically significant information.We also examined whether the female-male wage differential varies across the country. We expect these differentials to vary regionally because agricultural labor markets are regional ,4×8 flood tray cover different crops, have different lengths of employment, and employ workers with different demographie characteristics. Table 2 shows the estimates of the coefficient on the female dummy for each estimated region. Because the left-hand variable is the logarithm of hourly earnings, these values are approximately the percentage difference between women’s wages and men’s. We find large differentials that vary substantially across regions. The GME estimates are closer to zero in most cases and have much smaller asymptotic standard errors than do the two-step estimates. The sign patterns for the two estimators are the same except for piece-rate workers in the Western Plains. The GME estimates indicate that women are paid substantially less than men except in the piece-rate sector in the Western Plains and the time-rate sector in California and that these differentials are statistically significant using a 0.05 criterion.We are in an exciting time in Biology.

Genomic discovery on a large scale is cheaper, easier and faster than ever. Picture a world where every piece of biological data is available to researchers from easy-to-find and well-organized resources; the data are accurately described and available in an accessible and standard formats; the experimental procedures, samples and time points are all completely documented; and researchers can find answers to any question about the data that they have. Imagine that, with just a few mouse clicks, you could determine the expression level of any gene under every condition and developmental stage that has ever been tested. You could explore genetic diversity in any gene to find mutations with consequences. Imagine seamless and valid comparisons between experiments from different groups. Picture a research environment where complete documentation of every experimental process is available, and data are always submitted to permanent public repositories, where they can be easily found and examined. We ‘can’ imagine that world, and feel strongly that all outcomes of publicly funded research can and should contribute to such a system. It is simply too wasteful to ‘not’ achieve this goal. Proper data management is a critical aspect of research and publication. Scientists working on federally funded research projects are expected to make research findings publicly available. Data are the lifeblood of research, and their value often do not end with the original study, as they can be reused for further investigation if properly handled. Data become much more valuable when integrated with other data and information . For example, traits, images, seed/sample sources, sequencing data and high-throughput phenotyping results become much more informative when integrated with germplasm accessions and pedigree data. Access to low-cost, high-throughput sequencing, large-scale phenotyping and advanced computational algorithms, combined with significant funding by the National Science Foundation , the US Department of Agriculture and the US Department of Energy for cyber in frastructure and agricultural-related research have fueled the growth of databases to manage, store, integrate, analyse and serve these data and tools to scientists and other stakeholders. To describe agricultural-related databases, we use the term ‘GGB database’. GGB databases include any online resource that holds genomic, genetic, phenotypic and/or breeding-related information and that is organized via a database schema, and contained within a database management system , or non-relational storage systems. GGB databases play a central role in the communities they serve by curating and distributing published data, by facilitating collaborations between scientists and by promoting awareness of what research is being done and by whom in the community. GGB databases prevent duplicated research efforts and foster communication and collaboration between laboratories . As more and more organisms are sequenced, cross-species investigations become increasingly informative, requiring researchers to use multiple GGB databases and requiring that GGB databases share data and use compatible software tools. Use of common data standards, vocabularies, ontologies and tools will make curation more effective, promote data sharing and facilitate comparative studies .

Processing efforts congruent to the cloud infrastructure cannot be provided by the farm server

This documentation directly needs to fit for cross-compliance procedures with public authorities and personal calculations of the farm which requires a standardized format.Furthermore, the billing of tasks shall be processed simultaneously for contractors.To enable task or route planning for the MR or contractor a RRN is required which allows communication to the farmers also without internet.A digitally managed farm becomes resilient if it is characterized by utter independence of external internet connection and power supply.Like this, the FDFS is able “to prevent disasters and crises as well as to anticipate, absorb, accommodate or recover from them in a timely, efficient and sustainable manner”.Nonetheless, all online features and functions are used comprehensively in normal conditions but are replaceable by farm particular systems in case of intermitted power and internet connection.Consequently, a hybrid system was developed, combining cloud-based systems and farm-specific solutions.Likewise, it is for power supply, backed up by an emergency power generator, which is already mandatory for livestock farming.By its installation on the farm, it ensures the decentrality of the data repository.Storing data redundantly on different servers is the main aspect of configuring an FDFS in a resilient manner.Moreover, decentrality results in a higher level of data safety by securing data against external access and loss.All necessary data for the applied precision farming solutions are available in any circumstance.To ensure the connection of sensors, machinery, farm server, and farm management, during an internet outage, an LWN is installed.Less mechanized farms obtain digital farming technology through contractors or machinery rings.When a task is ordered by such latter farmer the contractor’s machinery connects to the LWN of the farm when it is in reach.Data transfer of e.g.prescription maps or documentation of tasks is then enabled.Communication between farm and contractor/machinery ring for task disposition can proceed before task execution with a minimum amount of data within an RRN of long-range like LoRaWAN.The single components of the FDFS in Fig.2 are explained in detail in the following sections.First a farm managed by an FDFS needs a power generator for a redundant power supply.

With technical hardware solutions,ebb and flow table it has to be guaranteed that in case of a power failure no voltage drop occurs.This can be achieved by bridging voltage drops with a UPS implementable for edge computing applications.Its capacities need to be selected according to the data and processing rates of digital sensors and devices running on the farm and the needed degree of resilience of the farm.Farmers have to decide which digital functionalities are fundamental for a similarly effective and sustainable maintained production without the internet thus balancing investment cost and necessary level of resilience.Fig.3 summarizes the general functions which could be covered by the farm server.The first main function of the farm server is to store relevant data for farm management.different data partitions are to mention: Basic data like field boundaries for guidance and calculations, AB-lines for controlled traffic, management zones for variable rate application, flight plans for drones, machinery settings, etc.Open Geodata, provided by public authorities, can be stored in an automated updated version or even several older versions if necessary for specific applications of farmers.Needed versions of satellite images are stored automatically corresponding to actual calculations of e.g.management zones.In level five this function can be conducted via AI, selecting and downloading the currently available satellite images according to the needs of the planned crop rotation.Depending on the cache function of each machine or sensor the server also is supposed to be used as a cache in case machinery or sensor has lost internet connection to send data to the cloud.Data sets of tasks that got interrupted by internet outages are completed in the cloud with cache data from the farm server when an internet connection is reset or over the landline.Some specific data, individual for every farm, might be needed in real-time where no delay by internet connection is acceptable.By receiving and storing these data, redundant to the proprietary cloud service, on the farm server ensures constant access to it.The format of these in real-time needed backup data on the farm server must be read- and processible for offline desktop software.Here ISOXML, JSONLD, and for some applications, Shapeles are the suggested formats.Shapefile and ISOXML are the most common in the actual applications of OEMs.

Data for later use could also be stored in manufacturer proprietary formats and be processed after reconnection to the internet in the proprietary cloud.When purchasing technology, which disadvantageously only offers proprietary formats it is favourable to see if there exists a plugin for converters like ADAPT.Generally, here a clear farm specific definition of necessary data has to be made by farmers because the duration of internet blackouts is never predictable.Among others because of a lack of interacting functionalities between different clouds.Nevertheless, some applications of predictions, machine learning, or simple AI algorithms are possible.In this case, farmers are reliant on the offline performances of their OEMs which evince a great deficit in this sector.Accordingly, secondary processing software needs to be chosen wisely.Focusing on long-term internet interruptions each farm needs to decide about which processed data in which time gap is indispensable.These diagnoses will reveal which proprietary raw data are expected to be made available by manufacturers in open and standardized formats for farmers to be able to use the data in secondary software while the usual FMIS is not available and a desktop version does not exist.Within the suggested FDFS, a desktop version of the FMIS is intended.This is a decisive contribution for maintaining effective, efficient, and sustainable crop production during any internet outage.The FMIS desktop version maintains the necessary functionalities of the specific farm needs.The ideal case of a resilient FMIS would be redundant to the online version which is closely linked to the processing efforts in the former section.Furthermore, interfaces to the LWN connect FMIS and machinery and ensure data transmission and documentation.The FMIS of the presented FDFS provides export functions of common, standardized, and open-source formats which ensures interoperability to secondary software like a GIS for example.When investing in a FMIS farmers need to consider which services their FMIS needs to fulfil in a situation when no internet connection is available.Data communication in between farm server and sensors/ machinery during internet outages is maintained by an LWN.Depending on farm size , topography, data traffic , and required latency of executed applications a corresponding network gets installed.

Farmers who depend on contractors or MRs additionally need to be able to receive and send a radio signal to communicate tasks while internet communication is not available.Otherwise, machinery rings or contractors cannot manage their orders to maintain maximum service capacity.This network does not necessarily need high bandwidth because for management purposes the contractor or machinery ring only needs basic information about the task.The range of this signal is defined by the catchment area of the contractor and MR.To establish and ease the installation of such systems only view standards with low installation costs should be taken into consideration.MRs and contractors, who work for the farmers, need to be able to connect with their machinery and sensors to the LWNs of their customers.When the executing vehicle is in reach of the LWN, the task data can be completely uploaded for task execution.When the job is completed, as-applied and further documentation data are sent to the FMIS through the farm server over the LWN.To complete the independence of the FDFS from external signals, the LWN also covers an alternative for positioning signals.That means all items on a farm, which use position data can navigate or track their position by calculating with signals of the LWN.In case the area covered by the LWN is too small for the farm extension, georeferenced marker points can be distributed on the farm area or mobile antenna stations can be set up.The main use case in the project MRdigital in Germany was in the field of slurry application by a subsidiary company of two machinery rings,flood table which acts as a contractor.A self-propelled slurry applicator is used.Via NIRS technology nutrient contents of the substrate can be measured which enables the system to conduct variable rate application using prescription maps.Such an applicator in addition to digital nutrient measurement is seldom purchased by single farmers and a fortiori, not by small and middle scaled farms.The business model to run such a machine within the organization and the management of machinery rings makes precision farming technology accessible and affordable also for small and middle scaled farms.According to the latter sections, usually, tasks and prescription maps are sent via the internet from the FMIS to the machinery ring or directly to the machine and vice versa.Farmers using an FDFS at their needed level can create and send tasks and prescription maps also during internet connectivity problems.The RRN enables the transmission of simple task data in advance.The prescription map and the as applied documentation data get transferred when the slurry applicator is in reach of the LWN of the farm.The machinery can read data from any kind of FMIS and write it back in the same format.The approach of categorizing farmers’ needs of resilience into five levels tried to meet the majority of conditions farms are exposed to.Nevertheless, many farms might prioritize another sequence of upscaling their level of resilience.For example, a farm with many sensors in the field acquiring low-volume data might prioritize the installation of an LWN before investing in a farm server.The five-level classification is supposed to give orientation for digitizing farms to prepare for crises.Technical setups are very individual and require therefore adapted solutions securing the most important digitized applications from failure.To set up an individual FDFS and choosing the right technical components might over strain the IT skills of most of the farmers, simply because it’s not their profession.Lachia et al.for example, found that the infield use of yield maps is too complex for over 50% of French farmers.

Assembling the components of an FDFS might be even more challenging.But having once set up an FDFS, the maintenance of its components, in addition, can also be expected as very demanding for farmers alone.Consulting and support from independent institutions are needed.A farm server as a central component of the FDFS covers many functionalities from storage to processing and AI to the management of access rights for third parties.This is also supposed to guarantee data ownership and control by farmers on whom has access and uses their data for which purposes.The more extensive these server functionalities get, the harder it can be for farmers to overview and control them.A lot of time might be needed for farmers to incorporate extensive server functionalities.But this should balance itself, the more functionalities the server covers, the more farmers will profit and the more time and money they might be willing to invest and vice versa.Considering that sensors and machinery have cache storage capacities to store raw data when cloud connection is lost, a large on-farm storage server might not be needed, especially if the farm is located in an area with a rather reliable internet supply.But here too, it depends on the applications farmers deploy.Areal imaging for example needs much storage capacities and sometimes low latency which makes it necessary to store and process the images on the farm server to be in time with the following application.The proposed FDFS can be a chance for small and middle scaled farmers in employing digital farming technologies.Only the investment of administrative components, like an FMIS, is sufficient and can, under certain circumstances, be built up modularly according to farmers’ needs and investment possibilities.Machinery and technology are rented or ordered from MRs and contractors.Here we meet the problem of lacking interoperability.When choosing digital components, farmers need to look out for solutions that use international standards on semantics and ontologies, in addition, to open APIs and data formats to ensure interoperability with future or external solutions.Developing such customer- or branch-specific solutions remains the responsibility of established agricultural software developers and solution providers.Solutions including proprietary data formats should be avoided.Also, the MRs and contractors need to take this into account when investing in new technologies.What is not defined in this paper is the detailed organization of data flow at the moment of an interruption of cloud connection.

Applying AI to the agricultural sector is not a homogenous challenge since the sector varies substantially

All interviews were held through digital meeting platforms and all but two were recorded with the permission of each respondent.The codes mentioned in Table 1 are used throughout this article to refer to the respective interview respondents.Remote sensing technology enables detection and monitoring of physical characteristics of the earth’s surface.Remote sensing data is collected from a distance, commonly from satellites and drones.The three most common properties of remote sensing data are spatial, spectral, and temporal resolutions.Spatial resolution is the pixel size of an image, a property that affects the ability to detect objects through imagery.differently, spectral resolution refers to the spectral sampling intervals size and number which affect the ability of the sensors to detect objects in electromagnetic regions.The temporal resolution regards the frequency of acquired data.The availability and economics of using remote sensing data collection is addressed by Khanal et al., which present remote sensing technology alternatives both open-accessed and for some cost.However, the resolution of the data varies, where the trend is that medium-resolution data is free whereas the prices for high resolution and very high resolution data increase in proportion to their increasing quality.Regarding data resolution, Meier et al. opine that site-specific smart farming depends on high resolution, as detection of anomalies are impossible or insipid with too large pixel sizes.Of course, depending on what kind of analysis the data aims to contribute to, the need for resolution varies.For example, predicting the crop yield within a field can accomplish a high accuracy despite a coarse resolution while detection of plant diseases through hyperspectral imaging requires a detailed resolution.Internet-of-Things is a collective concept for objects with incorporated electronics and connections that enable remote control and information sharing.In agriculture, IoT is mainly used for collecting data through different types of sensors.

By further data analysis,flower pot valuable information can be derived as decision support, e.g.for farmers.Kamienski et al. define four main challenges for IoT development in smart farming.First, the IoT system must have a high level of adaptability.Since the needs of farmers often significantly vary, the IoT system must be customizable to local circumstances but still not increase the required work for the farmer.Secondly, the IoT deployment must be efficient.As Kamienski et al. write, “there is no ‘one size fits all’ in IoT systems”.Thus, each system needs to be configured, the Internet connection and farm infrastructure must be reliable, and the farmer must deploy enough human and economic resources into this process.Furthermore, the scalability is affected by the previous factors but also depends on whether the system, and the models learned, are supposed to work for just one farm or entire agricultural consortiums.Lastly, the complexity of the IoT system can be interpreted as a trade-off between making the middleware broker complex and the software application simple, or the reverse.Another aspect to IoT in smart farming is security.Since the data often is valuable for the farmer and is regarded as a business secret, Kleinschmidt et al.describe the need for end-to-end encrypted communication from the sensor to the application.In practice, this means that the IoT sensor network must have a synced security strategy to the cloud database and the potential fog computing network.By ensuring security, the probability that the farmer trusts the IoT system increases.Still, trust in IoT systems does not just depend on security but also on the precision of the sensors.Without ensuring that there are no systematic measurement errors in the sensors, few farmers would trust the learned model or the real-time data.The potential of smart farming in animal husbandry, such as dairy-, beef- and fur production, is largely constituted by increasing productivity and profitability by streamlining and automating tasks and information.Much research consists of ways to monitor and look after the animals automatically or semi-automated.These articles suggest that devices, both wearable and non-wearable, may be incorporated in the animal stable and that these devices can gather data that can give indicators on the health of the animals.The data that may be gathered through these devices vary, but the wearable devices can measure heat, hormone levels, rut etc.The non-wearable devices more typically are 3D cameras for body condition scoring and infrared imaging, sensors that monitor environment and weather as well as automatic weighing scales and gates.In arable farming, an important feature of smart farming is to be able to calculate the vegetation index of fields or areas to be able to monitor when it is time for harvest and other activities.This may be done by both remote sensing and IoT solutions.

Viljanen et al. train a machine learning model aimed to optimize the “balance between the highest possible yield quantity and an adequately high digestibility for feeding”.By using an inexpensive drone system that can get multi-spectral data from an RGB camera and an infrared camera, traditional physical tools for predicting ley yield can be replaced by smart machine-learned models with higher accuracy.Furthermore, the research of predicting yield and quality of silage can also be accomplished through satellite data, as presented by Griffiths et al..The study shows that it is possible to detect mowing events of grasslands, and therefore characterize the land use intensity by looking at satellite imagery.In terms of yield prediction, Feng et al. stress the importance of incorporating biophysical characteristics of the crop in machine learning algorithms.This means that to learn a model with high precision, it is important to simulate the growing process of the crop to ensure that the model learns the crop characteristics in different stages of the growing process.Furthermore, Matos-Moreira et al.uses manual soil samples to further improve their model.By including manual sampling and analysis with a variety of existing data sources one may learn a model to predict the quality of a crop or the concentration of some matter at a given place and time.Another application of precision farming is to detect sickness or pests among crops.Torai et al. study how diseases can be detected in crops by classifying, or labeling, areas in pictures as “healthy”, “infected”, “diseased” or “aged”.Thereafter, methods such as hyperspectral imaging, Bayesian networks, and an analysis through probabilistic latent semantics are applied to detect the diseases .This study is a good example of a remote sensing technology applied to agriculture which needs a very high resolution of data, preferably on a scale of centimeters.One dilemma when applying artificial intelligence to arable challenges is how to use the different types of available data.Kerkow et al. use fuzzy mathematical modeling to solve this problem.This approach allows for mixing machine learned climate models with wind data and expert knowledge of the landscapes to build precise models .The literature review also brings up some interesting aspects regarding the implementation of AI in agriculture.Medvedev and Molodyakov highlight both theoretical and practical knowledge of smart farming as requirements for successful implementation.Unfortunately, seldom farmers have either the economic resources or the time to attend longer educations within the subject.To meet the lack of technical education within smart farming, Medvedev and Molodyakov propose smaller model-based courses that should cover technical, economic and management aspects to smart farming.

A crucial part of the education is that the courses are on-demand, so that busy farmers can access it whenever it suits them.Both business cases and clear driving forces are named as critical components to spreading the use of smart farming technologies in society.Barriers that hinder the drive towards smart farming are categorized as economic, institutional behavioral, and organizational as well as market.Furthermore, they identify social and moral drivers to play a key role in terms of creating a societal demand for smart farming.Without the support from society at large, innovations will not be adopted by key actors, they conclude.Other research aims to map the barriers to implementing and diffusing smart farming technologies.Kernecker et al., describe that farmers approach smart farming technologies differently given how much smart farming technologies the farmers have already adopted.The so-called adopters perceive the barriers to adopt smart farming technology as high investment costs, a difficulty in interpreting data, a lack of interoperability or precision in devices, that farmers cannot see the added value of the new technology or the relative advantage of the system, as well as a lack of neutral advice from advisors and other actors.The non-adopters also perceive high investment costs and unclear added value as barriers.Additionally, they regard too demanding complexity of use, that the technology is not appropriate for their context or farm size, as well as a lack of access to proof of concept from a neutral point of view, as obstacles.Finally, the literature review highlights the importance of data presentation and visualization, both in arable farming and livestock farming.Beside identifying possible applications of the technology in agriculture, several research groups argue that methods within machine learning and AI require decision support tools that visualize the data in comprehensive ways.One pattern, stated by a farmer respondent,berry pots is that farmers of different agricultural sectors almost always believe that the implementation of smart farming technologies has come further in other sectors than in their own.The agricultural sector that most farmers highlight as currently the most technologically advanced is the milk production.Milk robots were introduced to the commercial market decades ago, and with the milk robots the fodder of an individual cow can be customized, increasing its health status and production capacity.Due to the milk robots, the dairy industry is regarded notably data driven.One important aspect to consider when evaluating the success of the milk robots is the short feedback loop.Since cows are both fed and milked daily, the machines can adjust quickly depending on the latest input.Furthermore, Swedish dairy farmers have a long history of collecting data by being part of the so-called Kokontrollen, a cow data collection application owned by Växa Sverige.Even if Kokontrollen today is web-based, Swedish dairy farmers have been reporting to it for more than 100 years.Previously, all data was collected manually but today almost all data connected to milk production is automatically gathered by the milking robots.Contrasting to milk production, arable farming is diverse with different crops requiring distinct machines and technologies.Hence, a single successful machine is difficult to implement for the entire arable farming sector, making its technological development more complex.

However, it is possible to create effective technology for specific crops.As a rule of thumb, crops with high manual work, such as vegetables, use lots of technology since they operate on small, more controlled areas.In such environments, such as green houses, the feedback loop is faster and there are less uncontrollable factors, such as weather or wild hogs, which makes the application of new technology and AI easier.Of the three agricultural sectors compared in this study, beef production is considered by the respondents as the least technologically developed.Nevertheless, one respondent at a major company believes that meat production will have a central role in the development of the Swedish primary food production.The list of possible innovations includes making the value chain digital by automatically transferring information to the slaughterhouses regarding characteristics of the animals they will receive.By mandatory RFID tags for all cattle, the respondent argues there is an enormous potential, since the development of the animals could be followed in real time throughout the value chain.With such a system, the slaughterhouse could plan far in advance for incoming meat quality and volume.Simultaneously, a grocery store could send data to the farmers regarding the current popularity of different kinds of meat, enabling the farmers to adjust their production to the current consumer behavior.Furthermore, if one could autonomously and automatically weigh the cattle, their growth curves can be predicted which would enable optimization of the timing for sending animals to slaughter.By this optimization, one could avoid having full-grown animals that both drain economical resources and emit environmentally damaging methane gas.Regarding data and the activity of collecting data, the responses from the interviews reflects different realities within the agricultural sector.On the one hand, some respondents say that farmers generally are positive towards gathering data on their farm.On the other hand, some responding farmers state that they collect almost no data on their farms, although they say that they understand that data could add value to them.In-between is a spectrum of attitudes towards data gathering and implementation of technology in the farms.

Consequently both the rate of successful introductions and the volume of susceptible crops rise

Most of the land trusts in our study were created through the grassroots efforts of a few community leaders or environmental activists, organizing effectively to pursue shared conservation goals. Other land trusts and open space districts originated more directly through local government action, including voter approval.Few of the established programs in California enjoy a steady revenue stream for building large agricultural easement portfolios. Programs with substantial acquisitions have relied largely on fluctuating and opportunistic revenue sources, primarily state government funds and foundation grants. They generally lack the certainty that an ongoing, dedicated local tax source could provide. As a result, most programs acquire easements in fits and starts, limiting their ability to plan and work quickly with interested landowners.Landowners cannot be compelled to enter into an easement transaction by government regulation or eminent domain; selling or donating an easement is entirely voluntary. This means that programs must rely on each landowner’s understanding of the technique and personal estimate of benefits and costs. For many landowners, easements are a foreign or confusing concept. They offer the unwelcome prospect of having less control over their land and create uncertainties about the long-term consequences for immediate heirs and later generations of owners. Landowners located near rapidly urbanizing areas are especially reluctant to consider the easement option, as they believe that they will be able to prosper by selling their parcels for residential or commercial development sometime in the future. Finally, even with a supply of willing landowners, there is the challenge of fitting the available properties into a program’s criteria for location,ebb and flow agricultural quality and easement price. Nonetheless, the successful programs demonstrate that a few early transactions with landowners respected in the local farm community can break the ice for subsequent deals .

Primarily because they are non-regulatory and voluntary, easements on farmland increasingly appeal to landowners and communities attempting to protect open space and agriculture. With about 120,000 farm acres covered statewide, agricultural easements have become an important farmland protection tool in California in less than two decades. A small number of local land trusts and open space districts, assisted by funding opportunities and entrepreneurship, have established successful programs. Yet the active programs operate in only a minority of California’s major agricultural counties. Many of these areas lack easement programs because of the absence of citizen interest and mobilization combined with local government inaction. Most established programs also are limited in their easement acquisitions, largely because of unsteady revenues, limited entrepreneurship and reluctant landowners. Undoubtedly, the few successful programs will continue to grow and expand their easement holdings. But expanding agricultural easements to major agricultural regions is the key to making optimal use of the technique in California.Introductions of non-indigenous species of plants, animals, and microbes cause significant ecological and economic damage worldwide. A 1993 report from the Office of Technology Assessment estimates the monetary costs associated with biological invasions in the US alone is between $4.7 and $6.5 billion annually ; subsequent research revises that estimate for the US upward to over a hundred billion dollars a year . Moreover, since these estimates derive mostly from costs calculated for agriculture1 , there is consensus that these numbers are lower bounds. There are numerous pathways by which non-indigenous species enter a country: contaminated agricultural products, timber, potted plants, ballast water and packing materials are primary conduits for unintentional introductions2. We focus on introductions facilitated by commodities trade and explore how changes in agricultural protection affect patterns of trade and subsequent damage to local agricultural and ecological systems from exotic species introductions. In our stochastic model, exotic species introductions, success, and damage are functions of the volume of trade and agricultural production.

The model is coupled with results from international trade theory that link volumes of production and trade to agricultural policies such as output subsidies. This simple structure generates several compelling insights. First, we show that increases in agricultural subsidies may improve overall ecosystem health despite common opposition to agricultural protectionism by environmental advocates. This arises because protectionist trade policies in agriculture importing countries reduces imports, often from species-rich tropical regions, such that the rate of exotic species introductions will likely fall with reduced subsidies. Second, we establish that crop damages are a poor proxy for overall damages associated with biological invasions. Crop damage arising from biological invasions may rise as a result of increased protectionism, either because there are more crops available to be damaged or because there is more agricultural land available on which non-native species can gain a foothold. But because increased protectionism will reduce the volume of imports in agriculture importing countries, ecological—and hence total—invasion-related damage may nonetheless fall. Third, we argue that the ecological impacts of increasing agricultural subsidies may be markedly different for agriculture exporting versus agriculture importing countries. For countries that initially export agricultural products, increases in production subsidies will lead to an increase in both the volume of agricultural output and in the volume of trade, with unambiguously negative consequences for overall ecological health. In sum, the ecological consequences of raising agricultural subsidies are reversed in agriculture importing countries. In section 2 we discuss the relevant economic and ecological literatures. We then describe our model in section 3, derive results in section 4, and briefly conclude in section 5. Ecologists have studied the consequences of invasive species extensively; see Drake et al. , di Castri , Parker et al. , and Shogren for overviews. This research has established several patterns governing successful exotic species introductions.

For example, successfully introduced exotic species tend to be native to nonisolated habitats within continents and their success is enhanced with the similarity in physical environments between the original and exotic locations . Furthermore, species that inhabit disturbed environments tend to be successful at invading human-modified environments . These and other empirical observations suggest that predictions of the frequency and severity of exotic species introductions can be made on the basis of factors such as similarity in physical environments between trading partners, trade volume, and the extent to which the home country modifies its natural environment. Despite the extensive study of the ecological impacts of non-indigenous species, rigorous economic treatment of the problem is lacking. The little attention paid by economists to the problem of invasive species has focused largely on case studies and analysis of control and risk reduction methods . With the exception of Dalmazzone , none explicitly incorporate the role of commodity trade in their analysis. Using a linear regression model, Dalmazzone finds a strong, positive and statistically significant relationship between the ratio of exotic to native species in a region and both its GDP/capita and its population density. Indicators of disturbance such as percentage of land devoted to agriculture and pasture are also positively correlated . Dalmazzone finds weaker evidence of a link between susceptibility to biological invasions and engagement in trade: although she finds a negative and statistically significant relationship between import duties and presence of non-native species, the influence of other measure of openness such as trade as a percentage of GDP, volume of merchandise imports and tourism are all statistically insignificant. We believe these results underplay the importance of trade volumes for rates of exotic species introduction. Given the biological rules of thumb governing invasions, a superior econometric specification would decompose imports by type and country of origin, as recognized by Dalmazzone. Moreover, we believe empirical testing would benefit from a more thorough understanding of the mechanisms in which trade policies and flows affect introduction and damage rates. The present paper serves as a first pass at establishing these relationships theoretically. We explore the effects of agricultural protectionism,greenhouse benches via an increase in subsidies to domestic agricultural producers, on expected damage arising from biological invasions. It is shown that the magnitude of change in expected damage depend critically on two things: the responsiveness of damages to changes in agricultural output and the response of imports to agricultural subsidies. We show that offering subsidies in an agriculture importing country will reduce both its rate of introductions and the ecological damage caused by the introductions. We further demonstrate that changes in crop damage are a misleading proxy for the effects of protectionist policy on ecological and total damages arising from biological invasions. This latter claim is particularly important if we believe pecuniary losses to agricultural production are more easily observed than ecological damage from exotic pests and hence form the basis for policy decisions.

Proposition 1 makes the simple point that increased support for Home’s agriculture industry may reduce the rate at which exotic species are introduced because of the effects agricultural subsidies have on the volume of trade. For countries that import agricultural goods, production subsidies lead increased output of locally produced agricultural goods to displace imports, thereby reducing the overall volume of trade as the country moves toward self-sufficiency. As reduced volume of trade reduces the size of the platform for non-native species introductions into a country, the expected rate of introductions N, and consequently the number of non-native species that take hold J, is thereby reduced. Alternately, if a country instead initially exports agricultural goods, an increase in S raises the volume of trade since it induces greater agricultural output, exports of which finance greater imports of manufactured goods. Although there is a tendency to equate species introductions with imports of agricultural goods, trade in non-agricultural goods also frequently serves as a conduit for biological introductions, either through contaminated ballast water from ships, or infestations of packing materials and manufactured goods themselves. It is possible that λ may depend differently on imports of different types—we abstract from this issue in the interest of simplicity—nonetheless the larger volume of trade arising from subsidies in agriculture exporting countries increases the platform for introductions and so raises the expected values of N and J. Some simple interpretations of these classifications are useful at this point. Damages arising from loss of crops —either through infiltration of crop and pasture land by weeds or predation on crops and livestock by pests— increase as the level of agricultural activity increases. Commonly referred to as crop damage, these types fall under the definition of Augmented damage. Other types of invasion related damage are unlikely to depend directly on the level of agricultural activity. Introductions affecting marine and aquatic systems are good examples of this: invading mollusks foul water intake systems at power generation facilities; introduced fish out compete native species, creating losses to recreational activities such as sport fishing. In addition, there are numerous examples of exotic species displacing native species, with consequences for non-monetized assets such as ecosystem health and biodiversity. These examples meet the definition of Neutral damage. In subsequent discussion we will also refer to these types as ecological damage. If instead introduction rates are sufficiently sensitive to the volume of trade, then an increase in agricultural subsidies may instead reduce expected invasion related damage. This latter possibility raises an interesting problem. Since Neutral and Augmented type damages may change in different directions following an alteration in agricultural policy, then estimates of invasion related damage that are based on one type of damage serve as poor—even misleading—indicators of total damage. As noted in the introduction, most real-world estimates of invasion related damage derive predominately from estimates of damage to crops and livestock. However, as proposition 2 indicates, crop damage may be increasing while Neutral and Diminished damages, and hence total damages, are decreasing. This insight confirms our earlier conjecture: economic measures of crop damage are a misleading indicator of total damages arising from biological invasions. For the policy change considered here, an increase in the agricultural output subsidy S changes in crop damage overestimate changes in total damage, and may indicate an increase in total damage even when total damage is in fact declining. As outlined in proposition 3, the effects on an increase in S on damages in an agricultural exporter differ from the effects felt by an importer. Again higher S spurs local agricultural production, however for an agricultural exporter this finances greater imports.

Empirical results include the impact of UCCE’s expenditure stock on individual counties

Therefore, our results agree with the existing literature, which suggests that old expenditures impact current productivity positively, and their exclusion tells us only a partial story. The coefficients we have obtained in this study indicate that there is room for improvement in extension research and outreach, and that introduction of new research-based knowledge and technology improves productivity. Results also suggest that primary-occupation farmers may be less efficient than those who are able to maintain more than one profession. Efforts could be focused towards improving any existing gaps in efficiency among farmers in different counties.The results of our analysis can guide policymakers during a period of political pressure to reduce public spending for agricultural extension in the state. The county fixed effects results allow a more targeted policy intervention on higher and lower performing regions .By controlling for individual county and fixed-year effects that may be driving productivity in that county, we find that some of the major agricultural counties in California record high positive impacts of UCCE expenditures stock. Out of the 50 county offices in our study, we observe that UCCE expenditures stock has a significant impact on 21 counties for all values of knowledge depreciation. We observe a statistically significant negative impact on a few counties, such as Amador, Calaveras, Humboldt-Del Norte, Modoc, and Siskiyou. For two counties, the impact is not statistically different from 0. In terms of policy, these coefficients can be used as reference points for allocating budgets to different counties. Extension efforts could be targeted to the counties with inconclusive or negative impacts. Monetary impact of cutbacks on county productivity could also be calculated, using the estimates of extension expenditures in this paper. The analysis driven by county performance helps design policies with heterogenous focus, which has been more relevant when public funds have to be allocated among heterogeneous performing recipients of these funds. And finally, as shown in Section 5.3 extension introduces substitutability of traditional inputs with extension knowledge so that higher expenditure on extension in some of the lower-performing counties can substitute for other traditional inputs, dutch buckets for sale which may be scarce in supply.

In particular, our analysis highlighted and measured substitution of extension knowledge for labor and chemicals.Both biodiversity and the human activities that threaten it are unevenly distributed around the globe. Thus, evaluating whether they are spatially congruent and choosing the best areas for conservation actions given the distribution of these conflicts are central problems in conservation bio-geography . The magnitude of the current biodiversity crisis, coupled with the limited resources available for protecting biodiversity, implies that prioritization is unavoidable. Spatial prioritization seeks to identify the areas that are likely to yield the best benefits for biodiversity given a particular conservation investment. It may be applied at a variety of scales, including global , regional , national and sub-national levels. Spatial conservation prioritization analyses can be based solely on the distribution of the biological features to be protected . Alternatively, prioritization analyses can include socioeconomic variables that represent threats to biodiversity or opportunities for conservation, such as human population density, land cost and land use . Agriculture is the human activity that represents the main threat to the environment . It constitutes the largest land use on the planet, using 38% of Earth’s ice-free land surface and 70% of global human freshwater uptake. Food production accounts for 19% of Earth’s net primary productivity and 30-35% of global greenhouse gases, with direct impacts on biodiversity . The burden on the environment may be higher in the future as the human population is expected to increase to more than 10 billion by 2050 . Moreover, a billion people are currently chronically malnourished as a result of lack of access to food . Given the value of biodiversity for human well-being , understanding the potential impacts of future agricultural expansion on biodiversity is a key issue for humanity. The general aim of my PhD thesis was to evaluate the potential impact of agricultural expansion on biodiversity conservation during the 21st century. Specifically, I evaluated four interrelated issues: conservation conflict between agricultural expansion and the global biodiversity conservation priorities and the Brazilian system of protected areas ; the effect of incorporating agricultural expansion data into spatial prioritization models for the conservation of world carnivores ; and the benefits of a globalized conservation strategy for food production and for biodiversity conservation . The impact of future socioeconomic development pathways, including land-use trends, on biodiversity can be accessed by means of quantitative scenarios . For all analyses presented here, I obtained future scenarios of agricultural expansion from land cover maps produced by the Integrated Model to Assess the Global Environment .

IMAGE forecasts, at a resolution of 0.5° × 0.5°, the number of years that each area will be cultivated during the 21st century for six socioeconomic scenarios . For chapter IV, I also included an estimation of potential agricultural productivity in each grid cell, based on climate, relief, soil constraints and irrigation impact . For the first chapter, I overlaid the spatial polygons of the Global Biodiversity Conservation Priorities onto a grid with a spatial resolution of 0.5° × 0.5°. I tested whether areas defined by their higher vulnerability were more affected by agriculture in the year 2000. The opposite was expected for areas with low vulnerability . I also tested whether these priority areas would be more affected by agricultural expansion during the 21st century than expected by chance . To address the aims of chapter II, I overlaid the IMAGE’s land-use model with Brazilian protected areas to calculate the conflict between these two land uses. I obtained Brazilian protected areas’ polygons from the World Database of Protected Area . I also included 10 km buffers around each protected-area polygon to represent the legal buffer zone usually used in Brazil, which is an area where human activity is restricted. I then tested whether these areas were more affected in the present and in the future than expected by chance. Additionally, I tested whether there was difference between the integral protection protected areas and sustainable use protected areas . In both chapters I and II, I evaluated the probability of such conflicts to be found by chance using spatial randomization tests developed in R , considering 1000 iterations . To meet the objectives of chapters III and IV, I performed global spatial conservation prioritization using Zonation . Zonation’s algorithm provides a nested hierarchical ranking of the sites, maximizing the representation of species’ distributions. To define the ranking of importance of sites for conservation, Zonation analyses can also incorporate costs such as potential agricultural production. For all prioritization analyses, I defined the target proportion of areas to be protected as 17%, following the Convention on Biological Diversity , which proposed this percentage as the goal to be met by 2020. I obtained information about mammal species’ distributions from the International Union for Conservation of Nature’s Red List of Endangered Species. I overlaid the spatial polygons onto a grid with a spatial resolution of 0.5° × 0.5°. For chapter III, I focused on 245 terrestrial carnivore species. In chapter IV, I used 5216 terrestrial mammals. These taxonomic groups have been the focus of many conservation programs and they are often considered to represent a potential surrogate for other taxonomic groups . To test whether there is a spatial conflict between the global carnivore conservation solutions obtained in chapter III and the agricultural expansion, I performed spatial correlation analyses using the Spatial Analysis in Macroecology software .

The objectives of chapter IV were achieved by defining global conservation priorities considering three levels of political integration: individual countries, regions , and globalized . I also evaluated the effect of considering, or not, agricultural costs for spatial conservation prioritization. The different conservation solutions were evaluated in terms of the relative amount of food production lost by setting aside sites for conservation and the representation of the geographic distribution of species within those sites. I also evaluated whether the most underdeveloped countries would be subject to higher losses in food production under the global strategy. For this, I correlated the percentage of food production and area lost to sparing land for biodiversity conservation with three development indicators: the Human Development Index , the per-capita gross domestic product , and the percentage of GDP added by agriculture . I found that reactive global biodiversity priorities had about 49% of their area impacted by agriculture in the year 2000 . Conversely, proactive schemes had a low intersection with the agricultural distribution . By the end of the 21st century, there will be an overall increase in world agricultural area from 26.5% of the analyzed area in 2000 to 34.6% in 2100, according to IMAGE, and the difference between the proactive and reactive schemes is predicted to hold true. However, High Biodiversity Wilderness Areas, a proactive scheme,hydroponic net pots is predicted to suffer agricultural impact similar to the reactive schemes, with 73.5% of its area affected, if the worst-case scenarios are realized . In Brazil, a megadiverse country in which agribusiness is the pillar of economy, agricultural expansion is a major conservation concern . According to IMAGE, agricultural land use represented 22% of Brazilian land coverage in 2000 and is predicted to increase up to 40% by 2100, according to a business-as-usual scenario. Moreover, the percentage of protected areas affected is predicted to increase from 11% to 30%, with no difference between IPPAs and SUPAs . I found spatial conflicts between the best areas for terrestrial carnivore conservation and agricultural expansion in the 21st century . These conflicts were alleviated when I incorporated agricultural expansion information into the spatial prioritization process . Nevertheless, accounting for agricultural expansion resulted in a lower representation of species’ geographical ranges: the average proportion of represented ranges was reduced from 58% to 32%. This reduction affected mainly those species with small geographic distributions. In addition, the best solution for global carnivore conservation changed from a spatial distribution closer to that of the reactive global conservation priority schemes to one more like proactive ones. Looking at the impact of globalization for conservation and food production, I found that combining the use of agricultural expansion data and integrating countries in a globalized conservation blueprint to meet the 17% target for terrestrial protected areas, resulted in a 78% reduction in the costs of food production . Furthermore, this globalized conservation approach represented an increase of 30% in the representation of the species in the protected areas network.

The regional-scale conservation solution resulted in similar losses in food production, compared to the globalized solution, and an increase of 17.5% in terms of representation of mammals’ geographical ranges .Conservation actions in the different areas of the world should be planned according to the expected agricultural expansion in the 21st century. Some areas can hold mega-reserves , while other areas should focus on the development of wildlife-friendly agricultural practices. Within Brazil, my findings suggest that the risk of agricultural expansion should be included in the management of protected areas and associated buffer zones. Globally, conservation actions for carnivores should consider agricultural expansion because this may significantly influence the distribution of areas where conservation actions could be more effective in the future . The regional scale may represent an intermediate step towards the global integration. Economic agreements may evolve to common conservation policies, since this has already been done in the European Union by means of the Natura 2000 network . By comparing differences in the distribution of protected areas among countries in the different scenarios, I found that the poorest countries will not be negatively affected by participating in this globalized conservation blueprint. However, the particular cases in which poor countries would be impaired in their development process should be a focus of compensatory policies in order to guarantee the participation of these countries within the global approach. Moreover, such compensatory policies may help to overcome socioeconomic problems such as poverty and inequality, which are known to be detrimental to the success of conservation actions . Feeding an increasing human population, with rising per-capita consumption, while managing the environmental impacts of agriculture, is one of the greatest challenges for global policy. In my thesis, I demonstrated that agricultural expansion will continue to represent an important threat to biodiversity throughout the 21st century. Reducing food waste, increasing agricultural resource efficiency, closing yield gaps, and fostering organic agriculture are tools available for solving this challenge .

Prime-age adult mortality affects their production since their family business is labor-intensive

In the regions affected by Human Immunod efficiency Virus / Acquired Immune Deficiency Syndrome , prime-age adult mortality negatively affects household welfare by decreasing household income and consumption. Previous studies on the effects of prime-age adult mortality on household agricultural production show that the mortality decreases household size and productive assets such as land and livestock. In this study, we further ask whether prime-age adult mortality due to HIV/AIDS decreases the endowment of knowledge for agricultural production in Kagera, Tanzania, reducing total factor productivity. Equivalently, we ask whether prime-age adult mortality due to HIV/AIDS destroys household agricultural production by magnitude beyond the decreases in observed productive assets such as household members, land, and livestock. We also quantify how much decreased TFP growth contributes to the decrease in long-term household agricultural income growth compared to the decreased accumulation of each productive asset. Kagera was estimated to be one of the regions in Tanzania most affected by the HIV/AIDS epidemic , Beegle. Kagera is also the region where AIDS cases were reported first in hospitals in Tanzania. In 1983, the first 3 AIDS cases were reported and the number of cases increased rapidly to 5,116 cases in 1994. On the other hand, the share of reported AIDS cases in Kagera to Tanzania decreased from 100% in 1983 to 10% in 1994. In 2003, the percentage of HIV positive in Kagera among age 15-49 is 3.7% while the figure in Tanzania is 7.0% and thus HIV/AIDS pandemic in Kagera has been alleviated compared to other regions in Tanzania. We use the Kagera Health and Development Survey which collects the detailed information on households in Kagera in 1991-94 and 2003. The survey samples households hit by prime-age adult mortality more than households without the mortality and the data allow us to study the long-term effects of prime-age adult mortality on agricultural production. In the data, 36.7% of prime-age adult mortality is considered to be due to HIV/AIDS by deceased individuals’ families.

We will focus on agricultural production among other income generating activities and we will study the effects of prime-age adult mortality on agricultural production in the region. Agriculture is the major income source in Kagera and also in Tanzania. In Kagera, 85% of household heads engage in agriculture in 2000/01 while 70% in Tanzania , Tanzania NBS. In Kagera,grow lights households engage in subsistence and traditional agriculture. Male adult members produce coffee and banana with or without cattle manure. Female adult members produce crops such as maize and yams mainly for own consumption.As shown below, households hit by prime-age adult mortality between 1990 and 2003 have less increase in household members by 1 person from 1991 and 2003 than households without the mortality.They also accumulate less other productive assets; land and livestock. As a consequence, their agricultural income growth is also smaller. However, we do not find such clear differences in per capita asset accumulation and income growth between households with prime-age adult mortality and those without it. In order to explore the effects of the mortality on agricultural production more, we will study the difference in TFP growth. We study the hypothesis that a household hit by the mortality cannot increase TFP as much as a household not hit by it. We also decompose agricultural income growth into the contribution of the accumulation of each productive asset and TFP growth and compare the differences in those factors between households with and without the mortality. The remainder of this paper is organized as follows. Section 2 reviews the previous studies on the effects of prime-age adult mortality on households’ welfare based on household level micro data and the differences between the previous studies and this study. Section 3 outlines our conceptual model, hypothesis, and framework of empirical methods. Section 4 explains the characteristics of the original data, especially with respect to prime-age adult mortality, how we construct our data for the analysis from the original KHDS data, and discuss the relevancy of our specification of the model to study the data. Our empirical methods are explained in more details in Section 5 and the empirical results are shown and discussed in Section 6. Section 7 concludes this paper.Whether and how much HIV/AIDS epidemic affects a household welfare is the important topic. We can categorize the literature of the effects of prime-age mortality due to HIV/AIDS on household welfare into consumption studies and production studies. Beegle, de Weerdt and Dercon studies the effects of prime-age mortality on long-term consumption growth based on KHDS.

Their regression equations have change in logarithm of per capita consumption from 1991 to 2003 as the dependent variable and dummy variables for deaths as explanatory variables. They use household fixed effects methods in order to control unobserved time-invariant characteristics and relax the endogeneity and self-selection problem of HIV/AIDS as other previous studies based on panel data do. They take into account which year each death occurred by using dummy variables for deaths in 1991-1995, 1996-1999, and 2000-2004. Their results show that the coefficients of dummy variables for deaths are negative but only dummy variables for deaths in 2000-2004 are statistically significantly different from zero. This characteristics of the results are robust in various specification of regression equations. Their results imply that there are negative effects of prime-age adult mortality on consumption growth but households may recover from the negative shock of the mortality after 5 years. They find that a prime-age adult death results in a 7% drop in consumption in the first 5 years after the death. Carter, May, Aguero and Ravindranath use KwaZulu-Natal Income Study , South Africa data and study the effects of prime-age mortality due to HIV/AIDS on long-term growth rate of per capita consumption and find the negative coefficients for dummy variables for deaths although they are not statistically significantly different from zero. They also find the large magnitude of the negative effects: a prime-age adult death lowers a household’s 5 year growth rate by 21%. Although the consumption studies above find the negative effects of prime-age adult mortality on household 5-year consumption growth, channels of the causality has not been made clear. Production studies analyze some potential channels of the causality. Beegle uses the first 4 waves of KHDS from 1991 to 1994 and studies the short-term effects of prime age adult mortality in a household on the household members’ labor supplies. She constructs dummy variables for male and female deaths in future and past 0-6 months and 7-12 months and uses them as explanatory variables in regression equations. The dependent variables are the probabilities of being in wage employment, non-farm self-employment, working on coffee production, banana production or maize, cassava, or beans production. She finds coefficients of some dummy variables for deaths are negative and statistically significantly different from zero in regression equation of being in wage employment, working on coffee production and maize, cassava, or beans production.

Yamano and Jayne use two-year panel of rural Kenyan households and study the effects of prime-age adult mortality on households’ size and composition, crop production, asset levels and off-farm income. They find the mortality decreases households’ size, area under high-valued crops, gross and net outputs, farm equipment, small animals, and off-farm income. They find that the death of a male household head is associated with a 68% reduction in the net value of the household crop production implying large negative effects of the mortality on households welfare and that channels of the causality are decreases in productive inputs above. Chapoto and Jayne use nationally representative 3-year panel data in Zambia and find the results similar to Yamano and Jayne . These production studies show that the negative effects of prime-age adult mortality on household income and channels of the causality. HIV/AIDS also increases an household’s expenditure for medical care for the sick and funeral for the deceased. Tibaijuka finds that this expenditure is almost equivalent to the cash income for the 10 households in her data from Kagera, Tanzania. We can think that decreased income and increased expenditure for health care and funeral due to HIV/AIDS and prime-age adult mortality contribute to the decreased consumption which is found in the consumption studies above. Households hit by HIV/AIDS have to face tighter budget constraints and invest less in productive assets than the other households. Smaller investment in productive assets brings smaller income in the future. We contribute to the literature with the following three points. First, we provide an answer to the question whether prime-age adult mortality decreases total factor productivity in the long run. Previous studies do not ask this question although it is an important question to study the channels from prime-age adult mortality to decreased income and welfare. This question is closely linked to the question how important an adult’s knowledge stock of agriculture is for his/her household income generation. Since subsistence agriculture in Kagera, Tanzania depends on weather and is erratic,led grow lights the knowledge may be important. On the other hand, its agriculture is traditional and does not depend on new technologies and new market opportunities so much, the knowledge may not be important. If the knowledge is important, prime-age adult mortality destroys not only household members but also the quality of household as an agricultural enterprise. Second, we decompose the agricultural income growth into TFP growth and the contribution of each productive asset. Previous production studies analyze the effects of prime-age adult mortality on each productive asset separately and cannot show how much change in each productive asset due to prime-age adult mortality contributes to change in agricultural income or output.

We quantify this channel from change in each productive asset to change in agricultural income by estimating an agricultural production function and decomposing the long-term change in agricultural income growth into TFP growth and change in contribution of each productive asset for households with and without prime-age adult mortality. Third, we study the effects of prime-age adult mortality on long-term agricultural production and link the previous studies on long-term consumption with the previous studies on short-term change in production mentioned above in this section.We can categorize channels through which the mortality changes the investment decision into two: First, the household changes future asset accumulation path as a response to changes in current asset levels due to the mortality and inheritance. For example, the household may sell land and livestock in order to achieve efficient and smaller productive asset level as a response to decreased household members and productivity due to the mortality. Second, the household’s budget constraint becomes tighter due to the mortality and the household has to change its allocation of income into consumption and investment over time. The household lost labor for income generation since the member who was sick and deceased did not and will not contribute to the household as labor and other members take care of the sick and thus the household income decreases. Furthermore, the household faces expenditure for medical care and funeral. Tibaijuka finds that this expenditure is almost equivalent to the cash income for the 10 households in her study. Although there is no consensus on what adult age range we should use to study the effects of adult mortality on household welfare1, we set the age range for prime-age adults is from 15 and 50. In this subsection, we discuss the relevancy of this age range. Our focus is the effects of prime-age adult mortality on agricultural production. We will focus on prime-age adult’s death rather than other household members’ deaths since prime-age adults contribute to their household as main labor force for agricultural production and they are the age group who are affected by HIV/AIDS directly.We set the lower bound of prime-age adult to be 15 since 15 year old individuals is physically adult and start to face the risk of HIV/AIDS through heterosexual sex. Although under 15 year old children can contribute to their households with their labor, we do not think that decreasing the lower bound would change the results since most of them do not die due to HIV/AIDS shown below. On the other hand, we set the upper bound of the age range at 50. Figures 1 and 2 show the distribution of age by gender in the data. Figures 3 and 4 show the distribution of deceased individuals’ age by gender. Figures 5, 6, 7, and 8 show the distribution of age of deceased individuals due to HIV/AIDS .

The high production costs in dairy farming were attributed to feed purchased from external markets

They added that flock sizes were determined by resource availability, which caused annual variability, as competition for land resources for grazing in the region was increasing.Goat rearing was a largely seasonal activity, and it was more predominant in the summer or taken up based on the income needs of individual HHs.Herd sizes ranged from 10 to 55 goats per household, depending on whether goat rearing was a primary or supplementary source of income.Focus-group participants stated that goats were grazed mainly on lands with tree and shrub cover.Large goat flocks would require large tracts of grazing land.The scarcity of such land has therefore reduced goat keeping.Sheep-rearing HHs rarely depended on off-farm labor, whereas those rearing goats frequently depended on off-farm labor or agricultural wage work.HHs also depended on markets or the public food distribution system to meet their food needs, although dependence was greater for goat-rearing HHs.The least prevalent category was the CWDL system.Most HHs in this category were medium farmers , followed by small and marginal farmers.Only 4% were large farmers.Crop production in this system veered towards food crops, using seasonally available water resources, and limited external inputs.In this case, livestock keeping was integrated and intended to support crop production.According to the participants in the focus groups, this had been the most prevalent system in the 1990s.Diverse livestock species were reared in this system.Crop and livestock products were consumed predominantly at home, and only surplus production, if available, was sold.The primary source of income for these HHs consisted of remittances from family members working in cities.Land and herd sizes are presented in Table 2.Land and herd sizes differed across systems.HHs in the CSR system had the highest herd size in comparison to all systems.The CD, CSR, and CWDL systems had comparable land sizes.

The LWL system was not comparable to any system in both land and herd sizes.In this section, we present the results of the economic-performance study of only the CWL, CD, and CSR systems ,ebb flow table as they provided consistent income from agriculture.The revenue, costs of production, and total GM per household are displayed in Table 3 for all three systems under study.For HHs in the CD and CSR systems, GM comprised income from crop and livestock production.For those in the CWL system, it consisted of crop production and off-farm activities.The economic performance of crop production is explained by the various crop management and input requirements across systems.As indicated in the focus-group discussions, the CWL and CD systems limited crop production to the monsoon agricultural season each year, while the CSR system managed crop production for both monsoon and winter seasons each year.In terms of inputs, the CWL system incurred the highest crop production costs, followed by the CSR system and then the CD system.The differences were due to the types of crops grown, with cash crops having higher costs, associated inputs, and the availability of livestock manure, which replaced expenses for inorganic inputs.Detailed information about the production costs for livestock rearing is presented in Table 4.The costs for dairy farming were substantially higher than those for small ruminants.The CD system also exhibited the greatest variation in the GM.Some of the HHs in this system had negative GMs in the summer due to low milk production combined with high feed requirements by the cattle.The CSR system was the most profitable farming system, due to low feed costs and high market price for meat.The highest costs per annum in the CSR system were for animal health care and for leasing grazing lands.These factors nevertheless did not seem to impair economic performance.The CSR system managed to obtain a high GM in the summer, the most unproductive season in dryland regions due to high temperatures and water shortages.Another factor addressed in the survey was loan access and repayment.The findings revealed that HHs took loans from multiple sources to continue farming.Among the three systems, CD HHs had the most loans from cooperative banks , local pawnbrokers , and selfhelp groups simultaneously.The loan values were also higher in comparison to those of HHs in the other two systems.In the CWL system, HHs accessed government crop loans , self-help group loans , and government schemes to manage crop production.In contrast, only 50% of CSR HHs took loans, and only from cooperative banks.

Focus group discussions indicated that loans from self-help groups were entirely availed by women, however, loans from banks were from both genders, as women also had access to banks.Despite formal credit sources, informal credit sources are still being accessed particularly by the CD HHs.This situation can be related to the high investment and production costs in dairying farming, where formal credit options work due to pending loan repayment.The GM of the HHs in the CSR system was statistically higher than that of those obtained by HHs in the CD and CWL systems.There were no statistical differences between the CD and CSR groups.The linear regression analysis revealed that factors explaining the GM were dependent on the farming system.First, caste and family type were not significant in any of the farming systems.For the CWL system, land size was the only statistically significant variable clarifying the GM.For the CSR system, both herd size and land size were significantly and positively correlated with GM.For the CD system, however, none of these variables was statistically significant.In Fig.2 below, we further illustrate the relationship between herd size and GM, which helps to explain why herd size is an explanatory variable for the CSR system, but not for the CD system.For the CSR system, GM increased along with herd size, as indicated by the significant regression line.In contrast, the CD system exhibited high variation, as CD farms with low herd size obtained both negative and positive GMs, while those with large herd sizes obtained only moderate to low GMs.The CWL system had the lowest gains and no livestock.The characterization of farming systems in the study region revealed that the CWL, CD, and CSR systems were variants of intensive, specialized, and market-oriented farming systems, while the LWL and CWDL were variants of subsistence farming systems.The majority of the HHs in the region fell into two farm categories: CWL and CD.The CSR system, although lucrative, was dominated by the BC communities in the region, given that sheep rearing has been their traditional occupation for generations.

For LWL and CWDL systems, livestock rearing was a need-based livelihood activity, and it usually involved poultry and seasonal goat rearing.Although the CWDL system was the most prevalent in the past, the majority of HHs have now transitioned away from this system.Further analysis of the three systems revealed that the CWL system is a medium-input/low-output system, the CD system is a high-input/highoutput system, and the CSR system is a medium-input/medium-output system.In terms of economic performance, the CSR system showed the best performance, as explained by the low water requirements and low feed production costs.The profitability of this system was further enhanced by growing market demand and the current market price for small ruminant meat.The system also adapted to the dynamic context by adjusting herd sizes to the decreasing availability of common property resources.All these factors make the CSR system suited to the dryland context.Despite having the highest revenues, the CD system was less profitable, due to high production costs.This system exhibited high variability in GMs from moderate to substantially negative records across HHs.This variability might have been due to the influence of other factors not included in this study.In addition, the consistent income obtained from dairy farming came at the expense of crop production in the winter season, as scarce water resources were diverted for dairying.This strategy resulted in the loss of additional income for CD HHs, in contrast to those in the CSR system, which cultivated crops for two seasons each year, in addition to rearing small ruminants.These findings thus suggest that engaging in dairy production may not be a resilient option for HHs in semi-arid regions.The CWL system consistently exhibited low economic performance, with low revenues attributable primarily to higher production costs for cash crops and market volatility.In line with other studies such as, Sallu et al.; Ten Napel et al.; Ayeb-Karlsson et al.; Kuchimanchi et al.we find that the trend of intensification and specialization in farming, particularly in the CWL and CD systems, has increased generic risks and decreased flexibility for coping with disturbances and shocks.For example, the CWL system reflected the absence of crop diversity and livestock and was dependent on off-farm employment, which was not regularly available.

The lack of crop livestock integration at the farm level increases dependence on inorganic fertilizers , which reduces soil carbon levels, subsequently affecting soil fertility, crop productivity, and revenue in the long term.These factors make HHs in this system more reliant on external inputs and market conditions to continue farming, leading to higher risks in the long term.The CD system was the most desired by HHs in the region, as it provided consistent income throughout the year.However, this system had compromised GMs and can be seen as entailing high risk,hydroponic grow table as dairy farming is heavily dependent on external markets for feed resources, scarce water resources and milk collection.Small landholdings limit feed production and increase the amount of external feed that HHs are forced to purchase to guarantee production.Further, as reported in studies by Sishodia et al.and the Central Ground Water Board , the region is currently experiencing high water scarcity, and the situation is likely to worsen.In addition to being risky, therefore, the CD system may be economically unviable in dryland regions , contrary to general perceptions.For this reason, the promotion of dairy farming among poor HHs should be a point of concern for development programs, especially in dryland regions.In the farming systems examined, higher revenues were associated with higher costs due to increased use of purchased inputs, credit, and animal healthcare services.If these costs cannot be limited, they offset revenues, hinder profits, and perpetuate the ‘poverty trap’.In this study, this situation is illustrated by the fact that HHs in the CWL and CD systems had high levels of credit and debt, due to insufficient income and low profits.Increasing credit and debt thus pose a risk, as they are likely to become intertwined with farming strategies aimed at simply adopting a system and continuing to farm.Over time, this situation often leads to a range of social-ecological consequences , all of which perpetuate vulnerability to climate change.Although the CSR system has adapted into a modernized version of traditional small ruminant production, it is likely to be subject to further constraint due to dwindling common property resources and the decreasing availability of private lands for grazing in the future.Moreover, the scarcity of grazing resources has deprived low-income HHs of alternative and profitable livelihood opportunities from goat rearing.

The decrease in native poultry rearing is having a similar impact on these HHs, despite the presence of a niche market.In dryland regions, the current reduction in livestock rearing is leading to income losses, while also translating into decreased dietary diversity during lean periods and the loss of a critical buffer in times of drought or dry spells, as crop production is highly vulnerable to such threats.Characterization and economic performance studies like this one thus provide insight into the socio-economic and ecological dimensions of farming systems and support a more customized approach to agricultural development in dryland regions.Following, we discuss some outcomes from this study under the perspective of the WDP policy, given that it is India’s leading strategy for the development of dryland regions.Firstly, this study shows that 86% of the HHs are now practicing intensive market-oriented farming.Intensive systems are often associated with increased specialization and low integration between crop and livestock production, resulting in high-water usage and the doctoral thesis by Kuchimanchi.We thus infer that water resources generated through the soil and water conservation measures due to WDPs in the region are apparently being over-utilized by some HHs thereby decreasing the availability of water throughout the year.Likely, this also explains why only 38% of the HHs in the region have been able to adopt the CD system, or why the CWL and CD systems limit crop production to the monsoon season.A second notable outcome of the study is that, while intensification and specialization in farming have increased agricultural production, it has not led to economic prosperity.For example, the average daily per capita income for HHs in the CWL, CD, and CSR systems were USD 0.2, USD 1.2, and USD 2.4, respectively.These income values are lower than the per diem wage rate of USD 2.5, as prescribed by the Indian Ministry of Labor and Employment , and the World Bank extreme-poverty threshold of USD 1.9 day/person.Lastly, while WDPs tend to promote specific farming systems , that has induced changes in land use, cropping patterns, and livestock rearing in terms of herd size, animal type, and purpose.

The state of Iowa was chosen as the study area for its productive agriculture and eventful winter weather

The mean difference for good governance between organic and conventional farming systems was significantly different for sub-themes, mission statement and full-cost accounting.For instance, ‘Mission statement’ was significantly better for organic as organic farmers were aware of their cooperative certification and what it stood for.Similarly, in Ssebunya et al., the governance dimension recorded low scores.The sustainability performance of farming systems has several important implications for cocoa farm managers/farmers.The context in which farmers manage their cocoa farms has changed rapidly, often with little warning.The environmental specifications for producing cocoa, the socially stringent measures of abolition of child labour ensure fairness in labour conditions.These create uncertainty regarding future threats and potentials of producing cocoa through the organic or conventional farming system.This article emphasises the need to think about sustainability at the farm level at a basic level rather than the crop level.This underscores the need for improvement across the value chain.Notably, the paper highlights that farm level activities are within broader social and natural boundaries.An accurate picture of the sustainability performance of a farming system cannot be developed if these boundaries are ignored.Explicit recognition of these points in managerial decision-making would represent a marked departure for crop level that have thus far been reluctant to look beyond their walls.The SMART-FARM Tool provides the needed basis for measuring the economic, environmental, good governance and social impacts of farming systems.This, in turn, would help decision-makers better understand their sustainability risks and opportunities.This is needed because farming systems must be proactive in addressing any potential economic, environmental,vertical grow table good governance and social challenges that could emerge throughout their value chains.

Given the significant number and variety of these sustainability challenges, farming systems must prioritise the issues that need the most urgent attention.The sustainability performance of farming systems using the SMART-FARM tool provides a basis for developing comprehensive strategies to improve performance and informed decision making towards prioritising farm outputs.Implementing these strategies comes at a cost so that farmers need to tackle the inevitable tradeoffs between efficiency and adaptability.However, unless farmers master this challenge, they cannot ensure the sustainability of their farms.Climate change-induced weather anomalies, such as extreme droughts and intense rainfalls, have been increasingly observed in places where people are highly vulnerable to their various effects in recent years.Assessing the vulnerability and unequal coping capabilities to climate change and weather events has been a focus of research attention, for example, vulnerability to flooding , urban vulnerability to extreme heat , agricultural vulnerability to drought , to climate change , and to severe snowstorms.It is observed that climate change has caused polar cold air and anomalously cold extremes moving southward as a result of winter atmospheric circulation at high northern latitudes associated with Arctic sea ice loss.The increases in winter storm intensity and frequency are evident in the US, especially in both mid- and high-latitude zones , and have produced non-negligible winter weather-related losses.However, as one of the commonly seen catastrophic weather events, winter storms and their impacts are often overlooked and understudied.Winter storms have been recognized as one of the catastrophic events leading to agricultural damage and loss.In farming regions, severe winter storms such as blizzards, unending snowfalls and extremely low temperatures can lead to building damage, animal losses, and reduction in milk production.Winter storms on farmlands can also create other issues including the removal of fertile soils, traditional routines failure, and crops being wiped out.In the US, the Midwest is well recognized as a major producer of vegetables, dairy, beef cattle, and pigs.It is also a region that has experienced severe cold-air outbreaks and record numbers of snowstorms.

However, research is notably lacking in the vulnerability of farm communities to increasing winter storm events.The Intergovernmental Panel on Climate Change has contributed to assessments on climate change impacts, adaptation, and vulnerability since 1990 and created the distinct definition of vulnerability in 1997.Many climate-related vulnerability studies adopted the IPCC’s definition of vulnerability as a function of exposure, sensitivity, and adaptive capacity.The three vulnerability dimensions are defined as 1) exposure that characterizes the stressors and the entities under stress, 2) sensitivity that characterizes the direct effects of the stresses, and 3) capacity of the system to cope, adapt or recover from the effects of those conditions.Building on the concept of vulnerability, several investigators have advanced the characterization of the vulnerability components and approaches to assessing vulnerability.Among them, Hahn et al.constructed the Livelihood Vulnerability Index and categorized major indicators into contributing dimensions of vulnerability to evaluate livelihood risks specifically resulting from climate change.Since then, the vulnerability index further evolved with the replacement and addition of other indicators to suit local contexts and to be more relevant for target groups.There has been an increasing recognition of the linkage between vulnerability and five core categories of capitals including natural, physical, human, social, and financial capital.These capitals were described in the Sustainable Livelihoods Framework as resources used in the vulnerable context to cope with short- and long-term problems and have been integrated into indices to measure adaptive capacity.Despite various indices developed to estimate the level of vulnerability of agricultural communities to extreme weather events, suitable metrics of rural winter storm vulnerability remain under explored.To address the lack of vulnerability assessment regarding threats of winter storms in agricultural regions, this study identified rural areas of different vulnerabilities and explained factors leading to these differences by integrating local knowledge, existing indices, and statistical analyses.The synthetic vulnerability index developed in this study was anticipated to serve as a tool for adaptation planning and be adjusted to suit other climate-related vulnerability assessments or study regions.It is located in the Midwest of the United States between 40◦35′ N-43◦ 30′ N latitude and 90◦ 8′ W- 96◦ 38′ W longitude.

The state comprises 35.7 million acres, with over 85 percent of the land farmed, and has long led nationally in hog, egg, corn, and soybean productions.Iowa has an estimated population of 3.17 million in 2020 and maintains a diversified economy dominated by agriculture, manufacturing, biotechnology, finance and insurance services, and government services.There are 21 out of a total of 99 counties designated as metropolitan statistical areas in Iowa.Main metropolitan cities with a population of more than 100,000 include the capital city of Des Moines in Polk County, Cedar Rapids in Linn County, and Davenport in Scott County.Iowa is located in the heart of the blizzard belt and experiences frigid temperatures as well as dramatic storms in the winter.Average winter temperatures in the state could drop well below freezing, for example, even as low as below 6 ◦F in Cedar Falls-Waterloo, Black Hawk County.Most field investigations in this study were conducted in Black Hawk County, where about 133, 000 people reside in its twin cities of Cedar Falls and Waterloo.The vulnerability was analyzed at the county level for which the complete data was available.This study conducted several semi-structured interviews in the counties of Black Hawk, Buchanan, Kossuth, and Washington to obtain farmers’ narrated perceptions on winter storm impacts.This step is important because the interviews with stakeholders can provide the necessary information and knowledge in the local context.During January to February 2019, 14 farmers that produced different types of commodities were selected using a purposive snowball sampling approach so that they can represent main on-farm activities such as crop farming and cattle ranching coded in the North American Industry Classification System.Among the interviewed farmers, 5 operated diversified farms producing animal and crop commodities, 3 operated crop farms, 4 operated livestock farms, 1 was an orchard farmer, and 1 was a poultry farmer.Their farms ranged in size from 0.25 acres for a chicken farm to 500 acres for a livestock farm.Each interview took between 30 min to 1 h to complete the questions covering topics of the three key components of vulnerability assessment.A detailed list of questions is provided in Appendix A.While the visited places did not cover the entire state, they spread across different parts of Iowa, collectively enabling a comprehensive view of winter-related issues on farms in the state.Table 2 summarizes the winter storm-related impacts on farms and Fig.2 presents the frequency of content mentioned by respondents.They have revealed that, mobile vertical grow tables in the face of winter storms, Iowa farmers were mostly concerned about animal health, building damage, water and feed shortage, and power outage.

Efficient information delivery, insurance, and windbreaks were considered important in reducing storm losses.Additionally, blizzard, extreme cold, strong wind, and icing appear to be among the main threats associated with winter storms.Extreme weather can cause significant losses and damages such as decreasing yields and commodity quality levels in agricultural production systems.The interview results showed that farmers were exposed to losses from extreme winter weather such as winter temperature fluctuations and ice storms that threaten animal health and power supplies.The increases in storm occurrences and temperature variation lead to higher exposure.Event occurrences and temperature deviation have been used in previous climatic vulnerability assessments to represent the frequency of exposure and the level of changes in daily mean weather conditions.In this study, winter storm occurrences and winter temperature deviation were selected to measure the different exposure of Iowa counties to winter.The data on event counts was collected from the Storm Event Database provided by the National Centers for Environmental Information , which contains records on the occurrence of threatening weather phenomena.Various winter-related event types were considered in this study, including blizzard, cold/wind chill, extreme cold/wind chill, frost/freeze, heavy snow, ice storm, strong wind, winter storm, and winter weather.A Python script was created to calculate the total event counts for all counties in Iowa during the winter months of December, January, and February between 2010 and 2017.Winter temperature deviation was calculated using the minimum and maximum temperatures for each county downloaded from Parameter-elevation Regressions on Independent Slopes Model website which provides climate observations in the US at multiple spatial/temporal resolutions.From the interview results, it was found that the immediate impacts of winter storms came from affected on-farm structures and activities such as animal husbandry and building damage.Poorly constructed buildings appear to increase sensitivity to climate impacts.Animal health can be threatened by low temperatures and restrained freshwater access.Livestock farms are highly dependent on the climate conditions of a given year and they have to make considerable efforts to prepare supplies, implement actions, and recover in the face of winter storms.On the contrary, crop farms appear less sensitive during winter since crops are usually harvested back in the autumn.Thus, animal commodities sale and building age were selected as sensitivity determinants and represented using the 2012 farm sale statistics retrieved from the United States Department of Agriculture QuickStats and the 2012–2016 housing characteristics data collected from the US Census Bureau.Adaptive capacity is the ability to take actions and make adjustments to reduce adverse impacts resulting from climate-related hazards.The ability to cope with extreme weather events varies depending on assets, tangible and intangible, that support people’s livelihoods.These livelihood resources are seen as “capitals” and can influence adaptive capacity and thus vulnerability.Based on the five forms of capitals described in the Sustainable Livelihoods Framework, this study identified multiple adaptive capacity indicators from five dimensions: Natural capital.Farms surrounded by trees as windbreaks are assumed to be more protected from strong wind, therefore less vulnerable.This study used a georeferenced, raster-formatted and cropland specific land cover data layer downloaded from CropScape to extract pasture and tree cover in each county.Pastures with windbreaks were identified using a specified search radius of 200 feet as the recommended distance of a proper tree windbreak.Financial capital.Poverty has been included as a vulnerability factor.It is assumed that households with lower income possess fewer assets such as equipment and appliances that can help with the maintenance of buildings and animals.Thus, farm income and poverty were included as indicators for financial capital.The poverty rate and farm income for the year of 2012 were collected from the US Census Bureau and USDA QuickStats, respectively.Physical capital.Access to the Internet is considered the dominant way to collect all sorts of environmental knowledge to assist with decision-making.With sufficient Internet access, households can stay informed and are more likely to benefit from new policies and plans launched in real-time.This mirrors the qualitative interview results that have highlighted the importance of information.In this study, internet access was indicated by internet operations collected from USDA QuickStats.

Farmers in the Ada East District mainly rely on rainfall to cultivate crops

Studies indicate that gender, religion, class, and positions within households, and other cultural values also affect the uptake of information.As a result of the variety of social-economic and cultural factors which affect the uptake of forecast information, there is the need to focus on context-specific issues rather than wholesale generalizations of challenges.Hence, in Ghana, dissemination and farmers’ access to WIS has drastically improved.And the substantial body of knowledge on climate information science is developing in Ghana and elsewhere in sub-Sahara Africa.However, there is little evidence that WIS is applied in decision-making processes, including adaptive for smallholder farmers.Meanwhile, variability in climatic conditions affect farmers’ decision-making strategies, leading to low crop yield and increasing financial burdens on farmers in Ghana.We argue that the use of WIS for informed decision-making in farming requires an understanding of the usefulness and usability of WIS in terms of farmers’ definitions and perceptions.This knowledge gap is not well understood in the literature in Ghana and elsewhere in developing countries.Thus, providing WIS that is readily usable for decision-making in farming requires navigation and bridging any differences that might exist between what scientists/information providers perceive as useful and what is usable in practice.Therefore, this study examines the weather information services usability for farming decision-making with evidence from Ghana’s Ada East District.We organised the study into six sections.The study’s conceptual framework is presented in the next section, followed by a section on research methods.Subsequently, the study findings are presented in section 4,dutch buckets system followed by the discussion and conclusion in sections 5 and 6.

Usable information is defined in various ways to understand the relationship between information providers and users.We build on earlier definitions by attuning them to the farming context, where usable information is information that farmers are able to use as input for farming decisions.Although the terms useful and usable are often used interchangeably in the literature, they do not mean the same thing.Useful information is potentially relevant for decision-making, yet, because users may not know or may have unrealistic expectations about how it fits their decision-making, they may choose to ignore it.On the other hand, usable information is the knowledge that is readily applicable by users in the formulation of strategies under uncertain conditions like climate change and variability.Hence, although all forms of user-inspired knowledge are in principle useful, they are not always usable unless users and producers take specific steps to ensure that useful information is applied.On this note, it can be said that useful information relates to information providers’ outlook.In contrast, usable information pertains to users’ viewpoint about how applicable the information is for decision making in their context, considering factors such as availability of resources.It is precisely these different perceptions and understandings of useful and usable information between information providers and users that create the usability gap reflected in the low uptake of WIS.Dilling and Lemos distinguished the usability gap in climate information by indicating two broad areas: context and information production.For farming, context relates more to the farmer and issues arising from the farming community; for example, conservatism towards applying new information.Although this aspect of the usability gap is relevant, our study focuses on the information design and delivery aspect, which pertains to how information providers produce and deliver information to enable its usability.We build on Dilling and Lemos’ framework to develop analytical criteria for our study by attuning some of their factors with ours.

We expand on their framework, which focuses mainly on the formal scientific production of climate information services on a global scale.We do so by building five information design and delivery analytical criteria by adapting aspects of their framework and other new criteria derived from the literature to assess information design and delivery for farming.Local embeddedness refers to how information design and delivery connect with local farming conditions and context in a specific community.This criterion can relate to a situation where WIS is provided, including the knowledge of farmers, so that their unique characteristics, rules, farmers’ exposure to different sources of information, and information seeking and sharing behaviour are captured in the information design.Additionally, an information design with local embeddedness may include other relevant information design features such as agrometeorological indicators, agronomic tips, and so forth.The information should also be linked to farmers’ personal characteristics and social networks.Legitimacy denotes that information design and delivery conform to farmers’ interests, values, concerns, and perspectives, resulting in acceptability.Farmers may judge the legitimacy of the WIS based on who participated or not in its design and delivery.Here, the information design and delivery may consider several options, such as respect for farmers’ value and how the WIS connects to the contextual needs of farmers.Information providers may also attain legitimacy by maintaining mutual trust and respect.It also implies the alignment of the information to farmers’ local knowledge and values.Furthermore, the legitimacy of information can be affected if a forecast fails, is irregularly delivered, or is associated with long delivery chains and political biases.The temporal aspect of information design and delivery indicates when to expect specific weather conditions for farming, whereas the spatial resolution denotes the surface area for which information providers produce the forecast.The temporal criterion of information design and delivery may consider when the information delivery will be relevant to determine when to plough, sow seeds, or select crop varieties.Also, the presentation of timing as early onset, usual onset, and late onset in a seasonal forecast may be a relevant information design characteristic.

Information providers can tailor the information into high spatial resolution by integrating farmers’ local forecasts and analysing the implications of the projection with farmers.When the information design includes the delivery of high spatial resolution, some trade-off needs to be made between skill and scale criteria.We conducted the study in the Ada East District in the coastal savanna agroecological zone, where agriculture is the main economic activity.Agricultural activity in the district consists mainly of cultivating of vegetables, cassava, maize, watermelon, and other crops.Despite the relevance of farming for livelihood development and food supply to urban markets, the area experiences long dry spells, frequent dry spells, and low mean rainfall during the rainy seasons.The coastal savanna agroecological zone also experiences interannual variability interms of seasonal rainfall.In the area, the complex series of coastal/oceanic and atmospheric interactions including the role of the inter-tropical convergence zone contribute to uncertainty in weather conditions.These incidences have several implications, such as loss of planting materials, crop failure, and low yield.The Ada East District is selected for the study among other districts in the coastal savanna agroecological zones because the district is one of vegetable producing areas, including the Anloga-Keta area.Although the district shares the same climatic conditions with other districts, the availability of water to support the growth of crops is a challenge, compared to other districts such as the Anloga District and the Keta Municipality, which has groundwater available for farming throughout the year.In the district, the application of forecast information to support decision-making in farming is crucial.Despite growing research on the climate information sector in Ghana, most studies have focused on the Guinea, Sudan and the Sahal savanna agroecological regions.Hence, little knowledge exists on the provision of forecast information for farming in the coastal savanna agroecological area.

We argue that for the country to be food secure, there is the need to focus on regions especially, the Ada area, where water availability is a challenge despite its prominent role in the supply of food to rural and urban areas in Ghana.A qualitative research approach was applied in this study to establish rapport with research participants and use the findings to inform policy.Hence, in this study, we combined semi-structured interviews and focus group discussion methods to cross validate research findings and derived detailed information concerning the study’s objective.The qualitative research was conducted from June 2017–March 2018 in two phases, with results from phase one informing the organisation of the subsequent phase.The period stated above includes the performance of activities such as community entry, reconnaissance survey, informal conversations with farmers and stakeholders, and the actual data collection.The application of semi-structured interviews and FGDs as two phases of the research is described below.In the Ada East District, three farmers from each of the following communities were engaged in semi-structured interviews: Kasseh, Asigbekope, Bedeku, Ada Foah, Toje, Ocanseykope, Anyarkpor, Angorsekope, Dogo, Totimekope, Kajanya, Atortorkope and Tovie.At Detsekope, we interviewed one farmer, and at Kpodokope, we interviewed two farmers.This amounted to a total number of 42 semi-structured interviews in the Ada East District.With the assistance of agricultural extension agents and some community leaders, farmers were selected based on their availability, gender, dutch buckets use of WIS for farming, age, experience in farming, social status, and farming practices.We conducted interviews with either one male and two female household heads or two male and one female household head in each community.Participants in the interviews and the FGDs consented to partake in the study, and we assured them that their identity would be concealed in presenting research findings.The lead author conducted interviews in person, and respondents agreed that the researcher recorded the discussions.In the interviews, questions were posed on the types of WIS used, the extent of use, the ranking of the extent of use, explanations for a specific WIS’s choice over others, and other emerging issues were discussed.

The outcome of the semi-structured interviews informed the design of FGDs, to derive an in-depth understanding of farmers’ views concerning the types of WIS and the emerging factors which affected the usability of WIS for farming in the study district.Through the FGDs, we uncovered personal and communal attitudes, beliefs, and preferences of discussants concerning the types of WIS and their usability for farming.We designed the FGDs to elicit the interwoven factors that enhanced or obstructed WIS usability in the communities, when a participant indicated that an information provider delivered regular information, the group discussed and agreed on the definition.For example, guided by the lead author, discussants agreed that ‘regular’ could mean the daily or weekly provision of WIS.Overall, three FGDs were conducted in three communities: Toje, Anyarkpor, and Wassakuse.The three communities were representative of the three agricultural zones in the district, namely, the Kasseh, Big Ada, and the Ada Foah Zones.Through this cluster, we analyzed and derived general issues that affected the usability of WIS for farming in the district.The FGDs comprised eight to ten male and female farmers who were not part of the semi-structured interviews.At Toje, the discussants consisted of two older women, two younger women, three young men, and three older men.At Anyarkpor, there were nine participants in the FGD, comprising of three older women; one young woman; two young men; and three older men.At the same time, the FGD conducted in Wassakuse consisted of eight participants , for a total of 27 participants.There were no exclusive groupings of participants because, in the study district, women are allowed to freely express their views on issues in the presence of their male counterparts.Also, we sort to generate answers to the research in a context where participants could respond to multiple opinions.Thus, when a participant responded to a question, other discussants corrected or realigned some views together.Since we conducted the FGD purposively with different generational groups mixed together, we catered for the possible emergence of power and gender inequalities by calling each participant to express their opinion on a specific question.This approach helped to moderate the discussion and ensured that overactive participants did not dominate the entire discussion.We also called participants to vote on certain opinions, especially about the factors that enhance or obstruct the usability of weather information services and the ranking of the different information providers, including the district.Data analysis was carried out in three stages.The first stage involved the transcription of audio recordings of the semi-structured interviews and FGDs.We edited the transcripts by identifying the responses generated to specific questions, realigned sentence structures, and clarified the construction of some sentences.During this time, in addition to the field notes, we took notes on emerging issues.The next aspect of this stage involved grouping the transcript contents into specific identifiable themes.Second, we conducted inductive coding to identify the factors that affect WIS usability based on recurring words running through the transcripts.