Category: hydroponic agriculture

Although making a success of smart farming is already tricky and adding new complications might appear unhelpful, the task must be to recognize and confront the complexities, rather than sidestep or ignore them. The question is how to construct smart farming innovation processes that yield more effective configurations?To avoid producing misconfigured innovations, smart farming requires a reimagined innovation process. I argue the challenge is to imagine and realize an innovation process that can yield ‘emancipatory smart farming.’ Some clues of what such an innovation process might entail are provided by research on the possibilities of pursuing ‘responsible research and innovation’ in smart farming. With a focus on anticipation, inclusion, reflexivity, and responsiveness , RRI tries to respond to the new “socio-ethical dilemmas” called forth by contemporary technological developments. For example, as stated by Rose and Chilvers , “[i]n the rush to embrace smart agri-tech, we are in danger of forgetting the wider network of other innovations that play an important role, but may also affect societies in different ways” . One focal point of RRI is therefore “to stage reasoned deliberations on technological needs and concerns between historically marginalized food system actors and prominent decision makers in government” . An ambition is that RRI might become “a rubric for guiding innovation toward socially and ethically acceptable ends” . At issue is examining “interrelations between multiple co-existing innovations in sustainable agriculture [to] promote the cultivation of distributed responsibilities across wider innovation ecologies” . In the New Zealand dairy sector, for instance, RRI has recognized that smart farming will yield “adapted advisory structures, potentially leading to displaced farm staff and service providers” . Moreover, shifts associated with smart farming technologies might have a “major impact on the cultural fabric of what it means to be a farmer,” in part because they can entail “detailed monitoring by agricultural equipment makers, input suppliers, u planting gutter processors and retailers” . There are reasons to applaud RRI. It signals at least an interest in trying to integrate societal concerns in technical developments; and opens avenues for new engagements between groups that might not otherwise interact.

It is a close approximation of what an appropriate topological repertoire might look like because it emphasizes the visibility of stakeholders, actors, and material realities that otherwise can be marginalized or ignored. However, RRI in smart farming still fails to produce adequate configurations. It operates via a misplaced insistence that agricultural innovation can successfully reconfigure sociotechnical relations in one domain, without also pursuing systemic or structural change. In short, it is necessary to continue insisting on the need for reimagined smart farming innovation processes that work to sidestep the misconfigured innovations evident in today’s smart farming developments. A pertinent example of what might be possible here is the development of farm OS, which draws on activist engagements and explores how smart technologies can empower communities, through actions of solidarity and co-learning . The software helps farmers record, plan, and manage their operations. It is open-source, produced under a general public license, and is easily hackable, in contrast to proprietary farm management software. Farmers can integrate diverse tools, such as drones for capturing aerial imagery or sensors to record temperatures, and thereby retain latitude to configure their operations in astute ways. In its effort to unsettle established smart farming structures and enable farmers to take back control over the software and data they produce, farmOS resembles other efforts to hack and repair farm technologies . It also reflects a much wider societal shift whereby activists, community groups, or others in civil society embrace contemporary technologies and take advantage of the emergent affordances to pursue “productive resistance” . A key dynamic of digital life today is growing realization that ‘smart’ use of software platforms requires re-platformizing society so urban citizens as much as rural farmers can take advantage of technological affordances without reproducing a platform economy dominated by a few enormous firms . It is therefore illuminating that farmOS is part of a new partnership called Open TEAM that aims to create a platform to facilitate “soil health management for farms of all scales, geographies and production systems” . There is scope today for farmers and connected others to overcome the problems of ‘actually existing’ smart farming and the misconfigured innovations it churns out. ise from new possibilities on the technological horizon. Hitherto, a technical limitation on smart farming developments pertains to the uneven roll-out of high-speed internet access between urban and rural areas . But there is now evidence that 5G networking technologies using TV White Space or ‘frugal 5G’ could be a ‘game changer’ for rural Internet access. If there is to be a ‘what next?’ of smart farming, it will build on what we find actually existing today to create new possibilities for embedding food production within the wider ‘planetary cognitive ecology’ , with unpredictable outcomes.

One relevant near-term scenario emerges from research on the ‘internet of people’ , a term used by computer science researchers with a view to building on and improving the relatively passive ‘internet of things.’ In the “Next Generation Internet” they are exploring, the internet of things is not swept away but rather a “new reference architecture” is carefully-crafted onto it with a view to overcoming problematic features of the “current-Internet data-management paradigm [such as] constant monitoring of users’ behavior by global platforms to provide to them ‘navigation’ and filtering services to find relevant data embedded in the huge amount of available data” . The overall design calls for a “human-centric perspective” at the scale of implementation and a novel “data-management Internet paradigm” in which devices are proxies of humans and constantly exist in context and operate in self-organizing networks that create new efficiencies because the need for human decisions is minimized. Significant features include use of new 5G capabilities that enable relatively autonomous ‘device-to-device’ communications across ‘pervasive communities’ of connected users. Per an IoP manifesto , devices are designed to ‘be social,’ ‘be personalized,’ ‘be proactive,’ and ‘be predictable.’ The underlying notion is that the IoP will use new arrangements and practices to engender economic efficiencies and positive social impacts. But there is every reason to expect the types of behaviours and interactions proposed by this line of research to impact on ‘smart farming’ practices. Consider a hypothetical example of how the IoP might operate, which, in the absence of available real-world examples to use, I adapt from contributions to the IoP literature : Maxine is a dairy farmer and cheese producer. Her cheese sales are disappointing. She’s confused and worried. She searches online for new recipes. Her phone knows a new recipe or idea is needed [‘be personalized’]. It shares this info with devices belonging to Maxine’s friends [’be social’]. At a social event soon thereafter, a phone belonging to a friend of Maxine overhears2 someone called Sandy say that Maxine’s cheese reminds them of another cheese they ate on holiday in Holland. It sends Maxine’s device a message along these lines [’be social’]; shares the ingredients and recipe of the Dutch cheese [’be predictable’]; and suggests a tweaked production process [’be proactive’]. Maxine’s device also communicates with quasi-autonomous devices in the cheese cellar [’be social’] to produce a new test batch. Some months later, Maxine has produced the new cheese product. Her device then detects that Sandy will be nearby soon [’be social’] and arranges for a sample pack to be delivered to her [’be proactive’]. At the same time, Maxine’s phone arranges for sample packs to be sent to other people who match Sandy’s sociological profile [’be proactive’]. Their devices respond to say they like Maxine’s new cheese and Maxine’s phone sends them discount coupons for their next purchase [’be predictable’]. Today’s smart farming developments lay the ground for emerging operations in the IoP: devices such as phones, or sensors to measure soil moisture or temperatures, are now operating on farms all over the world; software platforms are integrating actions, collecting and analysing data, and providing pertinent information to guide decisions; and autonomous machines are already in action.

All of these arrangements of devices and sensors share information according to protocols and standards worked out in the context of today’s technological limits and possibilities. The scene is therefore set for new rounds of investment in technologies that adapt architectures and yield realities like those posed above. As such, tomorrow’s protocols and possibilities will build on the normality of devices and sensors contributing to on-farm intelligence and efficiency but with a view to delivering results impossible hitherto. As suggested by Maxine’s case, then, smart farming in the IoP still relies on human intelligence but the abilities of her farm operation to survive is upgraded and amplified by protocols and standards that grant proxy devices autonomy and intelligence to proactively prompt new connectivities and relations. Maxine’s relations with others are mediated, filtered, and ranked; her digital life draws on new affordances developing dynamically within pervasive communities operating across a proactive internet. Beyond notions of the ‘nanny state’ infusing debates about communitarian governmental action, the IoP scenario is more akin to people living with numerous devices acting like ‘guardian angels,’ planting gutter with autonomous device-to-device decision making based on assumptions about the needs and possibly the desires of the individuals ‘they’ oversee. Maxine may be conscious of decisions she makes to engage the internet and might even understand or be sent information about autonomous device-to-device activities pinging messages and moving data according to underlying protocols; but much of her social life in the IoP also unfolds without her active participation. It is a new rural scene; an image of a different society from today’s, not least because it suggests the arrival of a new cognitive ecology underpinned and driven by AI, with social relations played out in numerous colliding “regions of technical autonomy” . Taking stock of the IoP scenario, there is clearly a strong possibility that smart farming in this forthcoming context will unfold via further rounds of misconfigured innovations. The dynamics of capitalist accumulation will no doubt pervade the design of protocols, devices, and services. Per the orientation of practices within so-called “surveillance capitalism” , tech firms such as Google or Amazon – as well as agri-TNCs, with their new data science profiles – will explore opportunities in the IoP to construct a more predictable world. Their challenge will be trying to contain the latent capacities and chaos of human and non-human action within tight profitable parameters; to thereby reduce the scope for uncertainty and contingency to interrupt flows of decisions informed by populations of sensors laid out and communicating with each other according to algorithmic models of society.

However, the objective reality of space is that no computational architecture can make sufficient calculations to overcome the inherent and pervasive “chance of space” . Maxine operates in a contingency-laden context before any IoP devices arrive on the scene. What happens with digital technologies in general, and the IoP in particular, is simply now that “the chance of space swells” . In the IoP, Maxine engages a new rural scene that amplifies chaos with unpredictable outcomes. As such, unexpected dynamics might come to the fore. The distinction here is between the architecture of digital life and the actual lived experience of digital subjects, which always entails “intersections and recursive relationships” playing out via “iterative interplays” . As evidence from research on digital worlds demonstrates – and as I have discussed via reference to developments such as farmOS – contemporary and emerging digital devices and services provide affordances for subjects to use technology in unexpected ways, including for the sake of resisting oppressive social formations. In rural space, smart farming seen through the lens of IoP research might place new value on intelligent, efficient, and even to some extent ruthless practices that squeeze as much profit from land and labour. Nevertheless, and emphatically, outcomes of the technological shifts at play here remain unwritten. Like farmOS or other efforts to re-imagine smart farming technologies, the IoP might create scope for users to create new forms of cooperation, reciprocity, and solidarity. There is significant scope for further investigation into this emerging scene.

Even when we disaggregate the contract treatment by contract attributes, there is little evidence of significant differences. Yet, the plethora of null results arising from the heterogeneity analysis is informative regarding both the contracts offered to participants in the experiment and the larger non-experimental literature as a whole. The contracts offered by ESOP appear to  benefit households equally. The contracts did not disadvantage female headed households or older farmers. Nor did they advantage farmers with more experience or those who participated in training programs or were members of local farm associations. This uniformity is encouraging regarding the equity and achievability of the contracting terms that ESOP offers. In regards to the larger literature on the heterogeneous effects of contract farming, endogenous matching between principals and agents is extremely difficult to control in non-experimental settings . It may be the case that some non-experimental studies have confounded heterogeneity in contract outcomes with heterogeneity in contract participation.The final component of our analysis is to explore the robustness of our primary results to different samples, different inference, and different specifications. Table 9 summarizes these robustness checks and their results. The table also points to where in the Appendix the complete results for each check can be found. Our first check is whether our inference is robust when we account for attrition. Since some differences do exist between attritors and nonattritors in our sample, we calculate bounds for our estimates following Lee and accounting for the stratified and clustered design of the experiment. All point estimates for the treatment effect are bounded away from zero .

These results suggest that any potential bias introduced by differences between attriting farmers and returning farmers is small relative to our estimated effect sizes. Next, we check whether our inference is robust to corrections that account for testing multiple hypotheses. We adjust p-values for the FWER using the Bonferroni , Holm and List et al. correction, rolling bench along with adjusting for the FDR using Anderson sharpened q-values. Corrections for our main results are in Table B6 and Table B8 in the Appendix. In general, there is no change in significance from any of these corrections. In cases where our point estimates are significant, they remain significant when accounting for multiple hypothesis testing. Given the grouped nature of the randomization, leaving us with 107 treatment units, it is possible that asymptotic inference is unreliable. As an alternative method for interrogating the robustness of our results, we implement a randomization inference procedure outlined in Heβ . Where classical inference assumes the treatment is fixed and the sample is a random draw, randomization inference assumes the sample is fixed and the assignment to treatment is random. For each ANCOVA regression with covariates, we randomly permute the treatment indicator 5000 times, accounting for the stratified and clustered design of the experiment, which allows us to build a reference sample under the sharp null hypothesis of no treatment effect. We can then compare the distribution of outcomes when the hypothetical treatment effect is zero with the observed treatment effect and calculate p-values. Table B6 and Table B7 in the Appendix present p-values from the analytical standard errors presented in the body of the paper along with p-values calculated from the randomization inference procedure. Similar to our adjustments for attrition and multiple hypothesis testing, randomization inference does not move our results from significant to not significant for the pooled treatment. When it comes to the treatment effects for each contract type, there is a change in the effect of T1 on rice area, which is no longer significant. Besides this case, all other significant treatment effects remain significant, though at reduced levels.

Our next two checks are concerned with the robustness of our results to difference specifications. In Table 6 we presented results from regressions which included indicators for each treatment. We now conduct a pairwise comparison of each treatment against the control .Additionally, the OLS estimate without covariates of the effect of T1 on income per capita is no longer significant. There is no change in significance for any of the other variables and specifications. In Table 7 we presented Bonferroni-adjusted Wald tests comparing co-efficient sizes across treatments. We can make this same comparison in a pairwise fashion, directly testing T2 against T1, T3 against T1, and T3 against T2 . Any differences that were significant using the Wald test remain significant in the pairwise comparisons. However, we find that three cases where the Wald test failed to reject the null can be rejected in a pairwise comparison. Rice area for those in T2 is significantly less than T1 and income per capita is significantly larger for those in T3 compared to both T2 and T1. In each of these cases the p-value of the Wald test fell just below the 90 percent critical value and the switch to a direct comparison increases the precision of the estimates. Our final robustness check is to test our results using alternative measures of welfare. First, we disaggregate income into rice income, farm income other than from rice, and non-farm income. This allows us to determine if households are reallocating effort away from other sources of income and towards rice production. Tables B16 and B17 in the Appendix present results for the pooled treatment and each treatment arm. The results are consistent across tables: the treatment increases rice income but does not have a significant effect on other income sources. However, there is a small but insignificant negative effect on non-rice farm income. Despite this, it appears that contract farming increases household income without reducing other sources of income. Second, since income is notoriously difficult to measure accurately, we estimate treatment effects on two food security metrics.

The first is the Household Food Insecurity Access Scale and the second is the Food Consumption Score . The HFIAS measures a household’s feelings and perceptions of food insecurity and is the preferred measure of USAID. The FCS measures how often a household consumes food items in different food groups and is the preferred measure of the World Food Programme.Our results are generally robust to these alternative welfare measures . Farming contracts increase the FCS as do all three individual contracts. However, there is little evidence that farming contracts reduce the HFIAS. While some of the treatment effects on HFIAS are significant, they tend not to be robust to adjustments accounting for multiple hypothesis testing or attrition. Third, we estimate treatment effects on a back-of-the-envelope calculation of profits from rice production.Our data contains detailed inputs on rice production, including labor time. However, it lacks detailed price data on hired and household wages as well as input data on total farm production. Despite these limitations in the data, and the long-standing problem of valuing family labor, we can compute a rough estimate of profits earned from rice production. We use three different wages to calculate a range of rice profits. Based on data from ESOP, we calculate profits at a “low wage rate” of 1500 CFA per day and a “high wage rate” of 2000 CFA per day. We also calculate profit using self-reported “cost of labor” for rice production. Using these three sources to value both hired and family labor, we can create a range of back-of-the envelope calculations for rice profits per hectare. As can be seen in Tables B20 and B21 in the Appendix, farm contracts increase rice profits when self-reported labor is used but have no significant effect when we use the low and high wage data from ESOP. We believe that the lack of impact on profit using ESOP-reported wages is due to a lack of precision in calculated profit, and, as a result, a lack of precision in estimates. Using self-reported costs to calculate wage rates, standard errors on estimates are always below 1.0. However, standard errors on estimates using ESOP data are frequently above 2.0. We conclude that there is suggestive, though far from conclusive, evidence that the contracts did in fact increase profits.The results from our field experiment present consistent evidence regarding the impact of contract farming, though somewhat unexpected insights regarding the impact of different contract attributes. Participation in contract farming, or at least the contracts ESOP offered to rice farmers in our study, has a positive and significant impact on area planted, yield, market participation, and income. Obviously, this should not be interpreted as definitive evidence that all contract farming is beneficial to the agent, as contract terms will vary based on context, grow table hydroponic bargaining power, and the objective function of the principal. While the overall positive effect of a farm contract was expected, we did not anticipate some of the differences in outcomes across contract type.

In particular, contracts that provide extension training seemed to add no value above and beyond the fixed-price contract. Evidence from comparisons in Table 6 and our robustness checks all show that the provision of extension training frequently resulted in lower outcomes relative to the other contracts. Similarly, the estimates of treatment effects on input use does not reveal substantial differences between contracts that provided extension services and the contract that did not. Three factors may explain these results. First, extension training is expected to increase technical efficiency. However, many smallholder farmers are resource-poor and may be unable to apply the knowledge they have gained. For instance, training regarding best practices for the application of fertilizer when the farmer cannot afford to buy the fertilizer is time ill spent. Second, the farmers in our experiment had very basic levels of education. The extension training developed with ESOP may have been pitched at too high a level to be effective. Third, it may be the case that the extension training was too broad. Recent RCT evidence from Kenya and Nigeria has shown that significant improvements can be made to agricultural outcomes when targeted or personalized advice is offered . By comparison, broad or generalized recommendations typically provide no value added to farmers. That extension training was ineffective in our study is disappointing but not abnormal. Feder et al. , Bellemare and Jones and Kondylis all provide evidence that extension services in developing countries often prove ineffective in producing positive and significant outcomes for smallholder farmers. Furthermore, in many developing countries, extension services focus more on cash crops , neglecting staple food crops such as rice . While extension training proved to provide little added value, the simple fixed-price contracts turned out to produce particularly large impacts. Across multiple comparison groups, the fixed-price contract resulted in outcomes statistically indistinguishable from the contract that added input loans and extension training to the price guarantee. Focusing on the results of the Wald tests in Table 7, the contract that only offered a fixed price had similar effect sizes for area planted, yields, and income relative to the contract that added extension services and input loans. Market participation was the only outcome variable where the fixed price contract failed to meet or exceed the effect size of one or more of the other contracts. This result is striking in its simplicity and enormously encouraging in its implications for contract farming and rural transformation. It implies that the primary issue facing these farmers is output price risk. Though our experimental design does not allow for a clean test of the effect of eliminating price risk, since all contracts include non-price attributes, the preponderance of evidence suggests that providing a contract that eliminates price risk allows farmers to, on their own, make the necessarily investment to increase their rice area, increase their productivity, and, by selling more rice into the market, increase their income. Our results regarding the role of output price risk closely align with evidence presented in Michelson et al. and Michelson regarding contract farming schemes in Nicaragua. There the authors study contracts offered by Walmart and other supermarkets to purchase produce from smallholder farmers. They find that farmers who receive contracts isolating them from fluctuations in out price take on more credit, farm more intensively, produce more, and earn a higher income.

Various elements must be considered when estimating the cost of a crop. It divides agricultural costs into five categories and provides calculations for each. It also gives examples of how to figure out how much a crop cost. It is a theoretical article that always guide the implementation of estimating the cost of cultivation. This theoretical study was used in the proposed model to calculate the cost of cultivation. It was very helpful as it provided elementary description to calculate the costs for cultivation. The formulas proposed in this study was used in the proposed system to estimate the costs till the year 2028. From 2004 to 2015, the goal of this research is to evaluate the gap between various costs and gross value of output , as well as the trends of input utilization and critical factors for gross value of output of gram crop across top production states. The findings demonstrate so after 2009–2010, all states’ GVO and overall costs increased significantly. The commencement of the Government of India’s agricultural waiver scheme in 2008–2009 was found to be the cause of a large increase in operational costs from 2009 to 2010. It was also obvious that the compound annual growth rate was larger in 2009–2010 than in 2014–2015 when comparing 2004–2005 to 2007–2008. Profit margins were high in Madhya Pradesh and Rajasthan, indicating a cost-cutting trend. This work provided a comparative study about the costs between different states of India. The proposed work has used Ensemble regression algorithms that is used to forecast the costs till 2028. It provides a comparative study of a crop for a specific state from 2010– 2028. Hence the user would be able to identify the trends of costs from the year 2010–2028.

The forecasting is explicitly applied on the India’s cost of cultivation survey data from 2010–2018. This provides an elaborative view of operational cost,led grow lights fixed cost, total cost, Cost Concepts were displayed in form of a graph for better understanding of the trends of the costs. Agriculture is currently a dominant use of the land and a major driver of environmental change and thus agricultural landscapes are key to achieve the United Nations Sustainable Development Goals , such as food security and environmental sustainability . As social-ecological systems, agricultural landscapes reflect the intertwined interaction between humans and nature through time . The contribution of agricultural landscapes to society goes beyond provisioning ecosystem services . Farmlands can contribute with a wide range of other key ecosystem services to society such as regulating and cultural , while providing habitat for biodiversity . However, the social-ecological outcomes of farmlands relate to the characteristics of supporting landscapes, which ultimately reflect the management practices, i.e. farming systems prevailing at the landscape level . Distinct farming systems are characterized by different field- and farm-level agricultural practices, reflecting farmers’ decisions on crop selection, livestock management and/or the maintenance of non-crop elements . Managed under intensive FS, agricultural landscapes contribute mainly to food and fibre production, but at high costs for the natural environment . Agricultural intensification has been pinpointed as major driver of land use change, causing landscape homogenization, habitat degradation and loss, and the decline of species of conservation interest . Conversely, farmlands managed under low-intensive farming systems, especially those designated in Europe as High Nature Value farmlands , have been highlighted for contributing to a wide range of ecosystem services, beyond support to biodiversity .

Characterized by low levels of agrochemical inputs and livestock stoking, minimal mechanization and the rotational use of the land, HNV farming systems maximize the use of territorial resources for agricultural production, while promoting landscape level heterogeneity. Therefore, the maintenance of HNV farming systems has been related to the occurrence of species and/or habitats, among which some of conservation concern . The European Union Common Agricultural Policy has been recognizing the role of agricultural landscapes to meet societal environmental concerns, namely by explicitly defining specific practices that farmers’ should observe or by supporting low intensity FS fostering the nature value of agricultural landscapes . Overall, CAP instruments align with other EU policy instruments such as Nature Directives and the EU Biodiversity Strategy, which, among other objectives, aim to include agricultural areas under high-diversity landscape features and organic farming management through uptaking agro-ecological practices for a positive contribution of agriculture to biodiversity and ecosystem services . Still, while the link between agriculture and biodiversity and ecosystem services has been widely described, data-driven research assessing such relationship at the landscape level across taxonomic groups and services is still scarce. Data availability is a major limitation to advance knowledge on how farming systems shape biodiversity and ecosystem services at the landscape level . The Integrated Administration and Control System database, managed by EU Member States paying agencies to monitor and control CAP payments, has been highlighted as a potential source of information on farmers’ practices at the farm-level . Coupled with the Land Parcel Information System , a spatially-explicit identification system for agricultural plots, IACS provide a high spatial and temporal resolution dataset integrating several dimensions of agricultural management, such livestock stocking, crops and land use . The value of IACS goes beyond the support to assess and monitor the impacts of CAP instruments. This comprehensive source of high-resolution data has been increasingly highlighted for its potential to support data-driven research in agricultural landscapes . Examples include mapping HNVf , analysis of crop and landscape diversity , land use change , definition and analysis of farming systems , or modelling exercises to assess the impacts of policies on FS and biodiversity . However, studies using this detailed source of data reflecting agricultural management have seldom been performed to assess and monitor patterns of biodiversity and the delivery of ecosystem services at the landscape level. Farming systems conceptual and methodological approaches have been pointed as suitable tools to explore the links between farmers’ practices and biodiversity and ecosystem services .

Considering farms as systems and units of analysis, a FS includes a set of farms sharing similar characteristics, namely in what concerns land type, labor and means of production, reflected in cropping and livestock subsystems combinations and underlying management decisions such as livestock rates and crop types, or the use of fertilizers. Such characteristics result from farmers’ decisions, which are jointly driven by policies , socio-economic factors and by biophysical conditions . Ultimately, farmers’ decisions are reflected as the dominance of a given farming system at the landscape level . Thus, exploring the links between FS and biodiversity and ecosystem services is essential to improve our understanding on the impacts of agriculture on biodiversity in landscapes under different agricultural management. In this research, we contribute to advance the state-of-the-art, by exploring the link between FS and patterns of biodiversity at the landscape level. To do that, we used IACS data to identify and characterize the spatial distribution of farming systems in a region in NW Spain and explored the relationships between the composition of FS, and species and habitats richness. More specifically, we aimed to answer the following research questions: Can IACS be used to map and characterize different farming systems at the landscape-level? and, Is the occurrence of specific FS linked with higher levels of biodiversity? Results of the analysis between FS and targeted biodiversity indicators are discussed for the Galician region, and implications drawn with respect to the assessment and monitoring of patterns of biodiversity in agricultural landscapes, including High Nature Value farmlands, across the EU. Our study area covers Galicia, an administrative NUTS 2 region located in North-western Spain, between 41◦ N and 44◦ N latitude, and, 9.5◦ W and 6.5◦ W longitude with a total area of 29,575 km2 . Most of the region is characterized by an oceanic/dry summer climate and is located in the Atlantic biogeographical region, with only a small area located in the south-east Galicia integrated in the Mediterranean region. Characterized by an elevation ranging from sea level to ~2100 m in the western mountains and a hilly topography, roughly 36% of Galician municipalities have been declared as less-favoured areas according to the Council Directive 75/268/EEC . The biophysical characteristics and historical uses of the land of Galicia are reflected in a considerable natural capital, recognized by the designation of several Natura 2000 areas, including 16 Special Protection Areas and 59 Special Areas of Conservation . SPAs cover 101,135 ha and SACs 374,435 ha, corresponding to 3.4% and 12.6% of all the area, respectively. For the last half of the twentieth century, Galicia underwent a polarization between intensification of agriculture and forest activities in the best soils and marginalization of remote, vertical grow system mountainous areas, following the trends observed at the EU scale .

The specialization of agriculture targeted mainly dairy production, while in the forest sector focused fast growing species for timber. Thus, despite representing less than 6% of total area in Spain, Galicia currently accounts for about 40% of dairy and 50% of timber production in the country. Utilized agricultural area occupies about 30% of total area of the region. Holdings are mostly small family farms, considerably smaller than the Spanish average . Moreover, there is a large fragmentation of land property , implying that most farmers manage a rather high number of different plots of land. Thus, despite a trend for agricultural intensification along the last decades, the relatively small scale of farming units and the fragmentation of farm holdings in numerous plots of land contribute to the complexity and heterogeneity of the landscape. Together with a relatively high density of linear landscape elements, these aspects contribute to a High Nature Value linked to the occurrence of cultural landscapes in the area . IACS and LPIS data on farm-level management was provided by the regional managing authority . Linked to LPIS, IACS provides spatially-explicit information on the geographic location, area and crops produced and the number and type of livestock for all agricultural plots managed by Galician farmers’ and declared under the CAP payments. For this study, the dataset contained information for the year 2015 about 1,190,714 parcels, declared by 33, 009 farms . To assure statistical confidentiality, all farms in the dataset are identified by a randomly generated code and as no relation to any farm registration codes. Information about the distribution of EU Habitats of Community Interest in the region was derived from data available from the previous work of Ramil-Rego et al. . This data is the most up-to-date data source regarding the diagnosis, description and distribution of habitat types listed in Annex I of the Habitats Directive for the study area. Moreover, it constitutes the source of data for the Galician Natura 2000 sites regional Management Plan , and for the Standard Data Forms of the designated SACs in the region. While detailed habitat mapping has been carried out exclusively for Natura 2000 sites and their surroundings, records for the presence/absence of habitats in a 10 × 10 km UTM grid were available for the whole region from the same project. Here, we considered all terrestrial Annex I habitats listed for the study area , assuming that agricultural practices may have direct and indirect impacts on their occurrence at the landscape level. We used this information to calculate the total number of Annex I habitats and the total number of priority habitats recorded per 10 × 10 km grid cell . The diversity of species of conservation interest was derived from the Biodiversity Data Bank maintained by the Institute of Agrarian Biodiversity and Rural Development of the University of Santiago de Compostela. This data bank is the most updated source regarding species occurrence for Galicia, and includes information about the suitable habitats for each species, according to the methodology proposed by Ramil Rego et al. . Species of conservation interest were selected . Then, and converging with the objectives of our research, we selected only protected species associated with agro-ecosystems . Our final dataset included 119 protected species, from which 8 plants, 1 invertebrate, 11 amphibians, 13 reptiles, 80 birds, and 6 mammals . While unbalanced, the final set of species reflects the uneven number of species of conservation interest listed across the targeted taxonomic groups for the study area .

In tropical grasslands, moderate grazing led to soil carbon storage and resulted in greater productivity and soil water-holding capacity, potentially enhancing grassland resilience to climate change . Mixed grazing of different livestock species at moderate levels promoted higher diversity and ecosystem multi-functionality . Soussana et al. , who accounted for greenhouse gas budgets at nine European grassland sites, found that grasslands have the potential to offset a significant proportion of global emissions of greenhouse gases as a result of livestock grazing. These findings have significant implications for achieving carbon neutrality and carbon peaking. Altogether, integrating grassland grazing into existing livestock farming systems in China will undoubtedly meet the increasing human demand for high-quality foods and valuable ecosystem services.Despite the potential positive contributions to humans, grassland-based ruminant farming systems in China still face severe problems, such as low production output , greenhouse gas emissions caused by ruminants due to low-quality forage feeding , and grassland degradation caused by overgrazing . We determined that the key reason for the emergence of these problems lies in the lack of knowledge and techniques for integration of nutrition manipulation and grazing manipulation. In grassland grazing systems, herbivore foraging behaviors are a central feature in the animaleplant interface . Herbivore foraging selection can directly influence plant community composition and diversity by changing competition among plants, affecting grassland ecological processes and ecosystem functions. Simultaneously, foraging selections of livestock under grazing conditions directly determine the nutrient supply resulting from grazing and forage resources, hydroponic dutch buckets which are essential for nutrition management decisions.

Therefore, accurately determining the diet selection of grazing ruminants relative to available herbage is necessary for future developments of these techniques. However, complex variables create significant challenges for accurately predicting the foraging selection behaviors of ruminants under grazing conditions, making predictions extremely difficult. The challenges we explore are not exhaustive but are focused on key factors in developing sustainable grassland-based ruminant farming systems.Animals may selectively adjust their dietary composition according to their individual experiences, nutritional status, and physiological status. Previous studies have shown that ruminants tend to choose specific plants and plant parts when grazing . Decisions about what to eat are based on expected rewards and previous experiences, affecting an animal’s food preferences . In utero and early life experiences of livestock may alter food preferences through elusive epigenetic effects that drive the voluntary forage intake of animals later in life . For instance, Chadwick et al. found that lambs exposed to salt bush in utero grew faster and handled greater salt loads than lambs born from ewes grazed on mono-cultures of introduced grasses. Wiedmeier et al. demonstrated that cattle exposed to high-fiber forages early in life showed higher nitrogen retention and greater abilities to digest fiber during adulthood than cattle reared on low-fiber diets. Available evidence has also suggested that ruminants selectively balance their diet according to their nutritional requirements. For instance, grazing ruminants maximize their energy intake through selective feeding on forages, during a day , or within a specific feeding schedule . Mineral-deficient cattle and sheep have been observed eating soil, licking urine patches, eating fecal matter, or eating dead rabbits, whereas animals without mineral deficiencies may sniff or lick these items but rarely consume them . Furthermore, ruminants may selectively feed based on their satiety and changing physiological needs, such as growth, lactation, pregnancy, parturition, and weaning .

Herbivores selectively consume individual plants based on their chemical composition, determined by secondary compounds, nutritional ingredients, and flavor substances. Typically, herbivores prefer to feed on plants containing only modest amounts of secondary metabolites. In small quantities, these compounds reduce bloating and improve protein utilization, immune responses, resistance to gastrointestinal nematodes, and reproductive efficiency . However, excessive volumes of secondary metabolites in grassland plant species limit the forage chosen for ingestion by grazing livestock. For example, goats selectively reduce their intake of forages with a greater composition of secondary compounds such as tannins . Lambs choose to reduce meal size and increase meal intervals when their diet is high in terpenes . Animals generally forage based on flavor profiles and nutrient content, especially when consuming different components of available plant anatomy . The spatial distribution of plant populations, plant species richness, and spatial relationships among different plant species in natural grasslands affect livestock foraging behaviors. Specifically, sheep increased consumption of high-preference species when low-preference species followed a clumped distribution rather than a random distribution . High plant species richness enhanced the frequency that animals switched diets and weakened the ability of herbivores to choose food, increasing foraging costs and interfering with the herbivore’s choice of foraging . Herbivores consumed the largest number of palatable plants when 3 plants species were segregated into 3 patches independent of each other. In contrast, the total forage intake of herbivores for all plant species was reduced when the 3 species were homogenously distributed through patches in a spatially equal neighbor relationship .

Local environments can influence dietary selections by grazing livestock. Extensive studies with pen-fed ruminants have shown that the ambient temperature affects feeding willingness ; however, very little empirical data is available describing the mechanisms affecting herbage intake in grazing ruminants under variable temperature management. Nonetheless, the physiological consequences of heat or cold stress may be similar for ruminants under housing or grazing. In beef cattle, feed intake increased at temperatures from  15 to 28  C but decreased at temperatures above 28  C . For grazing ruminants, behavioral responses amplified the effects of temperature stress on herbage intake under heat and cold stress . Studies examining the relationship between temperature stress and foraging willingness have primarily exhibited inconsistent results. Adams et al. observed significant reductions in the herbage intake of grazing cattle under acute cold stress, whereas Beverlin et al. found only minor changes in herbage intake for cows experiencing changing temperatures . Due to the inconsistent results, whether or not temperature stress affects forage intake in grazing ruminants remains ambiguous. The most relevant measurements to date are also relatively short-term, making it difficult to make recommendations on adjusting intake predictions. The effects of ambient temperature on forage intake may also be amplified by other environmental factors, like wind or precipitation conditions. For instance, wind velocity exacerbated the effects of low temperatures on grazing ruminants during winter but could help alleviate heat stress in summer . Previous studies also reported that rainfall events temporarily reduced the intake of grazing cattle by 10% to 30%, decreasing the cattle’s average daily gain . In addition, terrain plays a principal role in the forage selection of grazing livestock . When examining geographic and environmental factors affecting livestock feeding behaviors, cattle tended to avoid foraging on pastures with slopes greater than 10% inclines during grazing trials, and the number of grazing animals decreased as the slope increased. On slopes with a greater than 60% incline, almost no animals were found foraging . The location of water sources and social behaviors also affected foraging selection by grazing livestock .In addition to variable environmental factors, grazing regimes established by managers are also vital components of estimating forage intake in grazing animals.

Kitessa and Nicol found that cattle continuously co-grazed with sheep showed a lower herbage intake than those rotationally co-grazed with sheep. Perez-Ramirez et al. demonstrated that decreased grazing time of dairy cows strongly increased the pasture intake rate and decreased the foraging selectivity relative to available herbage. Congruently, dairy cows were more motivated to forage when receiving a low-supplement feeding regime. Furthermore, Savian et al. found that the sheep undergoing rotatinuous stocking exhibited an increase in daily herbage intake and improved bite rates compared to those under traditional rational stocking management, suggesting that rotatinuous stocking maximized the herbage intake and optimized the grazing time of sheep. Nevertheless, the factors influencing livestock dietary selections are also constantly changing ,bato bucket as grazing animals are foraging in a dynamic world. Herbage production, grassland community composition, and plant chemical composition fluctuate considerably in response to changing environmental and climatic conditions . This perspective has been supported by a longitudinal study of arid rangelands . Over the 37-year study period, herbage production varied annually between 4% and 307% of the median value. Simultaneously, temporalespatial variations in the herbage’s chemical composition, richness, evenness, and biomass led to heterogeneity. As a result of grazing, preferred plants and specific parts of the plant’s anatomy are removed from the landscape over time, leading to continuous changes in the plant community composition within pasture environments . The complex influencing factors, combined with continuously changing grassland herbage resources , make foraging selections of grazing ruminants extraordinarily complex and unpredictable.In the future, continued research is necessary to clarify and explore the mechanisms underlying foraging behavior, thereby helping us better predict grazing ruminants’ diet selection. Farmers, landowners, and livestock managers have an obligation to embrace vital agricultural and technological advances and establish new nutritional assessment methods for grazing ruminants. Implementation of nutritional intake assessments based on foraging selection behaviors assessment will allow stabilization and increased production in livestock agribusiness. Consequently, our knowledge regarding the nutritional requirements of grazing ruminants requires greater refinement. Many additional elements have been identified to affect the nutritional requirements of grazed ruminants , so knowledge gleaned from animals kept indoors cannot be extrapolated to ruminants in outdoor pasture environments. Further data collection and longitudinal studies are necessary to build mechanistic models of the nutritional requirements of grazing ruminants in terms of energy, protein, minerals, vitamins, and water. Continued research should focus on the critical factors controlling grassland ecological and productive functions.

Biodiversity has been identified as the primary determinant of grassland ecosystem services and functions . Experimental studies have shown that the stability and functionality of grassland ecosystems require high plant species diversity and multi-trophic species diversity for co-regulation like that of below ground soil microbial diversity and above ground community diversity . Improving grassland biodiversity by systematically managing grazing practices is critical for grassland ecological service advancement. Therefore, future studies should target identifying mechanisms of grassland biodiversity maintenance and grazing regulations on grassland biodiversity, offering a scientifically reproducible basis for guiding grazing manipulation practices . It has long been recognized within livestock production practices that rumen microbial communities play an essential role in improving ruminant production and health. The rumen microbiome, consisting of bacteria, protozoa, fungi, archaea, and viruses, composes a sophisticated symbiotic network essential to the maintenance, immune function, and overall production efficiency of the host ruminants . Digestion performed by the rumen microbial community accounts for up to 70% of the total dietary energy in ruminants. The symbiotic metabolic operation between a ruminant host and the rumen microbiome results in many end products critical to other biological processes, including developing the rumen epithelium and establishing the immune system . Ergo, improving our understanding of rumen microbial ecology in grazing ruminants promotes enhanced animal production efficiencies. Considering nutritional manipulation for grazing livestock should also be a management option to optimize rumen microbial communities. Ultimately, this in-depth examination of livestock grazing management and nutritional practices aims to provide a blueprint for developing sustainable, grassland-based ruminant farming systems by integrating animal behavior analysis, animal nutrition principles, and grassland ecology, to achieve “winewin” outcomes for grassland ecological/ecosystem functions and livestock production policies .