Month: September 2023

Posted on

Previous studies have also observed correlation between thermal stress and agricultural yields

While any fractional cover estimate will be subject to error, we believe MESMA is an improvement over these other models for reasons that will be discussed in Section 4.2. Finally, we do not believe that the errors in temperature estimation that result from errors in fractional estimation will preferentially affect any specific crop species. Therefore, while there will be pixel-level error due to fractional cover estimations, these errors should balance out and allow for relative stress comparisons when analysis of crop temperatures is aggregated to the field and/or species levels. Fifth, differences in flight timing will contribute to noise in the interpretation of results. Crop stress fluctuates with time of day, and plant transpiration has been found to plateau midday and then decrease in the afternoon as soil water content and soil water potential decrease . Therefore, temperature residuals are not only a function of the overall health of the crop during the year and season the imagery was collected but also the water availability on the day and time it was captured. While flight timing should be considered when interpreting findings, we do not believe that the effect is significant enough to overwhelm the yearly signal of increasing stress that is likely due to drought. Temperature residuals were shown to increase from 2013 to 2015. If this trend were due to the timing that the data were collected, we would expect the timing to have a similar trend as the residuals. However, the flight in 2014 was flown latest in the day yet did not have the highest residuals. Additionally, 2013 and 2015, which were flown at similar times of day, had the greatest differences in average LST residual, which is the opposite result as would be expected if flight timing were the main driver of residuals. Therefore, flight timing is an important factor to consider when interpreting LST patterns, but its effect on this study is assumed to be small. If designing a study to compare water stress between months or years,blueberries in containers consistency in timing of acquisition would enhance interpretability of results. Sixth, this study assumed that environmental variables such as air temperature, radiation, and wind did not deviate significantly across the study scene.

In our study area, which has little variability in elevation and has similar vegetation types throughout, we assume the climate is relatively stable. However, some variability will exist across space that will cause error. If this model were applied to a study area that encompassed multiple climate zones, thermal endmembers would need to be calculated separately for each zone to account for known differences in environmental conditions. For this reason, we suggest that this method be implemented over relatively homogeneous areas, with respect to topography and climate. Finally, this model requires a priori information about the landscape and informed knowledge to select appropriate thermal groups. In this study, a crop map that was compiled on-the-ground at the county level was used to inform thermal classes. In lieu of a similar map, crop information could also be gathered from a classification of VSWIR imagery or from a national crop map such as the Cropland Data Layer . With that information, crops could be separated into groups that are similar physiologically and bio-physically such that they are expected to have similar thermal behavior. One of the limitations of the model is its sensitivity to choice of thermal groups. While we expect this method to be transferable to other regions, types of landscapes, and thermal groupings, further work would be necessary to test this hypothesis. This study found a significant inverse relationship between green vegetation and LST. Other studies have observed similar trends between temperature and vegetation . However, unlike other models that use vegetation indices to account for this relationship, such as the VHI or WDI, our model uses a mixing model. The mixing model is an improvement upon the vegetation indices as it leads to a more accurate estimate of fractional cover in a diverse landscape such as our study area. NDVI is sufficient for estimating fractional cover in simple landscapes with little spectral variability, but a mixture model is better suited for estimating fractions in spectrally and spatially diverse landscapes . NDVI is also sensitive to variability in soil background reflectance, which is accounted for with a mixing model. MESMA, in particular, has the added benefit of using multiple endmembers per surface cover component, which allows these components to be grouped by spectral similarity. We found that there is significant thermal variability within the broad surface covers of GV, soil, and NPV.

These findings imply that a detailed knowledge of the landscape, beyond basic surface cover fractions, should be considered for interpretation of LST in agricultural areas. We explored thermal patterns in agricultural orchards and found that core thermal differences exist between crop groups of citrus, perennial fruits, and nuts that may not be attributable to stress. These findings indicate that physiological differences between crops result in different thermal behaviors that will impact interpretability of stress. Results are also in agreement with the observation of Roberts et al. , who found that LST and GV cluster by dominant plant species in LST/GV space. Moreover, we found that there is thermal variability in soil that is correlated to soil albedo and in NPV that is hypothesized to vary by structure. This result is consistent with studies of soil moisture, which have observed that moisture will lower the albedo and temperature of a dry soil . The method detailed in this paper acknowledges the variability within soil, NPV, and GV and uses a crop map and MESMA endmembers to account for some of this thermal variability. Current remote sensing methods that estimate agricultural stress either require field specific inputs that limit the scale of applicability or are wide-reaching but too simplistic in their assumptions such that all GV, soil, and NPV are treated similarly regardless of structural complexity, albedo, or functional group. Field-level models such as the WDI and CWSI account for differences in species by requiring crop-specific data, but these intensive inputs limit broad spatial analysis. Other models that have been developed for analysis across large areas, such as satellite-based ESI and VHI, do not account for the degree of thermal variability that was found in this study to be present in an agricultural landscape. While ESI and VHI do use NDVI and, in the case of ESI, also LAI to account for thermal differences within green vegetation, these parameters will not account for thermal variability from soil type or moisture, NPV, or even species-level thermal variability. An advantage of this method is that it segments GV, soil and NPV into groups that should have similar thermal behaviors while requiring no site-specific inputs other than a crop map. Its internalized calibration makes this method scalable across time and space.

In lieu of a crop map, MESMA endmember groups, such as were used for NPV and soil, may be a suitable substitute for grouping GV into thermal classes. However, further study would be necessary to test this hypothesis. Monitoring crop stress is important in anticipating the future of the agricultural landscape and can provide insight into plant water status and plant stress that could help to identify unhealthy crops to mitigate impacts that could lead to decreased yields and economic losses. We found that thermal imagery collected at only one date per year over three years of drought was able to identify the species that were facing the highest degrees of stress, in agreement with county-level yield data. Thermal remote sensing has been observed to correlate with fruit quality in orchards with open canopies and has been used as an indicator of regional agricultural drought as measured by crop yields . Subsequent analysis of our model would be necessary to determine if the results are robust at the field level,planting blueberries in pots but even the regional correlation with measured crop yields has important implications for farmers, policymakers, and scientists analyzing food and water resources. Moreover, temperature patterns were correlated with expected ET rates for crops in California in a dry year, further bolstering the hypothesis that LST patterns can be used to infer information about crop water use and stress. We hypothesize that deviations in the expected linear relationship between ET and LST are the result of irrigation management decisions such as irrigation method, timing, or applied amount that will affect the health and productivity of the crops. ET rates were calculated under the assumption that crops were watered with surface irrigation systems, suggesting that expected ET rates would increase by 3-6% if drip or micro irrigation were applied . As the frequency of drip irrigation for orchards has increased dramatically in the past couple decades, this change in method may be a factor in deviations from the ET/LST relationship. We also hypothesize that deviations in the ET/LST relationship are a factor of drought management techniques such as reduced watering. In line with this assumption, walnuts consistently had the lowest temperatures and residuals of the three nut crops in the study. This finding is consistent with suggested drought irrigation management techniques that recommend against deficit irrigation for walnuts as they are highly susceptible to damage if faced with water stress and are not as tolerant to these practices as either almonds or pistachios . The lower walnut temperature may, therefore, be the result of continued, consistent irrigation in comparison to almonds and pistachios, which receive deficit irrigation.

The temperature residuals capture the difference between expected and measured temperature and therefore act to as an important source of information about ET rates, the irrigation management practice, and stress. These thermal analyses are important for prioritizing water resources, especially in times of drought when water is limited. Additionally, maps of thermal stress could be valuable to assess the representativeness of in situ measurements of carbon dioxide, water vapor, sensible heat, or other fluxes over a heterogeneous landscape . This method of quantifying stress could also be complementary to surface energy balance models such as Disaggregated ALEXI and Mapping Evapotranspiration with Internalized Calibration . As our approach provides a relative measure of stress and DisALEXI and METRIC estimate actual ET, the surface energy balance models could be used to test the sensitivity of our approach to changes in ET. Alternately, the segmentation of soil and NPV into thermal classes in our method may be of use for refining the evaporation component of DisALEXI or METRIC for increased accuracy in crop ET estimation. Our approach has the added benefit of requiring only VSWIR imagery, thermal imagery, and a crop map that provides a level of detail of, at minimum, plant functional groups, and these minimal inputs allow for ease of implementation. Given these inputs, the approach suggested in this paper is probably best suited for agricultural applications at a spatial scale where environmental variables do not vary highly, such as the study scene in this paper. Within a relatively homogenous area, this method could be applied routinely using consistent thermal groups in order to identify which fields are most stressed and/or to gain information about irrigation management practices, particularly during drought as studied in this paper. In order to improve the ability of this method to capture within crop change in drought stress through time, further analysis would need to be conducted with a dataset that collects at approximately the same time for every capture. With our dataset that contained large differences in time of capture, these timing differences made such analyses beyond the scope of this paper. This study showed that thermal signatures of agricultural crops are correlated with crop species and fractional cover. Therefore, LST data on its own without information about surface structure and composition is challenging to interpret in the context of crop stress. The SBG mission would provide spatially and temporally paired thermal and VSWIR imagery globally which would allow for detailed analysis of LST patterns that take into account fractional cover and surface type. The ability of SBG to monitor crop stress would be enhanced by a crop classification that could group crop fields into relevant thermal classes, removing the need for accurate GIS data layers.

Posted on

The AVIRIS results are analyzed for portability and band importance

MASTER is a thermal sensor that captures 8 bands of emissivity between 4-12 μm, used to represent the proposed SBG thermal bands . The AVIRIS data was resampled to a resolution of 18 m while the MASTER data was resampled to a 36 m resolution. This paired dataset was flown over a portion of the Southern Central Valley seasonally while the state experienced severe drought effects. This unique dataset allows for study of remote sensing capabilities while also providing valuable information as to the response of crops in California to drought. The goal of this dissertation is to use data from the HyspIRI Airborne Campaign to evaluate how hyperspectral and thermal imagery can be used to improve upon current initiatives to account for and manage food and water resources in the face of a changing climate. This research will study patterns of agriculture and crop water use in the Central Valley as they shift throughout the course of an intense drought period from 2013-2015. These patterns will be investigated using imaging spectrometry from AVIRIS and thermal imaging from MASTER by mapping crops into relevant water use groups and then analyzing three indirect measures of crop water use from the imagery: choice of crop plantings, land surface temperatures, and water vapor patterns. Moreover,large plastic pots this dissertation will serve as a proof of concept for actively monitoring and measuring agriculture from space when the proposed SBG satellite is launched. In Chapter 2, I use three hyperspectral images acquired from AVIRIS over the course of the 2013-2015 drought in the Central Valley of California to both evaluate the performance of hyperspectral imagery for crop classification and to study farmer decision making with drought. A random forest classifier is run on the AVIRIS imagery to classify crops into groups of similar water use. Results are then compared to equivalent classifications using Landsat Operational Land Imager and Sentinel-2 imagery.

The results of this classification are then used to study the prevalence of crops as they change with increasing drought. Analysis highlights the economic and environmental drivers of planting decisions, and what this means for the future of California agriculture. In Chapter 3, I use spatially coincident AVIRIS and MASTER imagery from 2013, 2014 and 2015 to study the health of perennial crops over drought. First, I use a mixing model on AVIRIS imagery to decompose the scene into its fractional makeup of green vegetation , non-photosynthetic vegetation , and soil. Next, I model the expected temperature of each pixel as the fractional linear sum of its thermal components. I then calculate a thermal residual for each pixel as the difference between its measured temperature from MASTER and the modeled temperature. This method strips away thermal variability due to air temperature, time of day, fractional cover, structure, and moisture to allow for direct thermal comparisons between pixels and crop species. Thermal variability within agricultural fields is quantified and crop health is assessed. In Chapter 4, I evaluate spatiotemporal patterns of water vapor as they occur over agricultural fields in the Central Valley to evaluate the potential of this imagery to assist with agricultural applications. I use pixel-level column water vapor estimates derived from AVIRIS radiance imagery, surface characteristics obtained from AVIRIS reflectance imagery, and interpolated maps of wind to investigate relationships between the atmosphere and the surface. I propose and test a set of hypotheses for how water vapor will interact with the landscape in a diverse and complex agricultural scene at the pixel, field and scene scales. Results and analysis further knowledge of opportunities and limitations for using water vapor imagery to better understand crop water use. Although California faces substantial variability in inter annual precipitation and is accustomed to multi-year dry periods, the 2012 to 2016 drought was exceptional in its severity, and may be emblematic of greater shifts in California’s climate associated with anthropogenic warming .

Climate projections for California indicate that mean and extreme temperatures are likely to increase over the next century, which will increase the risk of experiencing future droughts of the severity of the 2012–2016 event . Future droughts will undoubtedly continue to put strain on water supplies, but the magnitude and extent to which these events impact water resources will depend not only on the characteristics of the drought, but also on the adaptive responses of people . In California, where the agriculture sector uses roughly 80% of the state’s managed water , agriculture simultaneously shows high vulnerability to a warming climate while also offering the greatest opportunity to mitigate the intensity of future drought impacts through adaptation strategies . Consequently, it is critical to study how we can monitor crop management response in real-time in order to assist with policy making during drought and analyze the ways in which the long-term sustainability of food and water security can be improved. This research used annual hyperspectral remote sensing imagery to assess the accuracy at which imaging spectroscopy can be used to map crops into categories of similar water demand and analyze changes in cropping patterns in a portion of the Central Valley. The study takes advantage of data collected over three years of a multi-year drought as a unique opportunity to measure agricultural response and adaptation in times of drought. Climate change is likely to significantly affect regional agricultural patterns and crop yields , in part due to management decisions such as fallowing fields or switching crop varieties or species . Therefore, monitoring how crop patterns change during droughts is a direct measure of adaptive response. Cropping decisions impact society in multiple ways by altering regional water requirements , food yields , economic production , and pesticide exposure . Consequently, accurate and timely crop maps are necessary to support long-term adaptation planning for a broad range of sectors, and are of use to farmers, managers, policymakers, and scientists.

Remote sensing has the potential to map crops and monitor changes in crop area more efficiently and frequently than time and labor-intensive on-the-ground crop accounting. Hyperspectral imagery, which samples hundreds of spectrally contiguous wavelengths, has the potential to identify crops at a single time point with a single sensor at higher accuracies than a broadband sensor . This ability is critical to enabling managers and scientists to stay abreast of rapidly changing planting choices and assess current risks, which is a need that current mapping initiatives with remote sensing are unable to fulfill. Most remote sensing mapping initiatives in the United States rely on satellites such as Landsat and the Moderate Resolution Imaging Spectrometer because of their large spatial and temporal coverage, ease of accessibility, and free availability . The National Agricultural Statistics Service ’s Cropland Data Layer is the most comprehensive current agricultural mapping initiative for the United States with an easily accessible crop map published at yearly intervals at a 30-m resolution . It relies on data from Deimos-1,raspberry container the United Kingdom’s Disaster Monitoring Constellation 2 , and Landsat 8 Operational Land Imager and produced an overall accuracy of 81.1% in California in 2016, with accuracies of crop groups ranging from a low of 32.8% for berries to 77.6% for forage crops. Although widely used and highly useful, the CDL has limitations concerning reproducibility and timeliness. First, by using three sensors, not all of which produce publicly available data, reproducing this map or using this methodology on a different study area or at a different time would not be possible. Furthermore, with maps published at the end of each year, the CDL does not offer near real-time or mid-growing season assessments of crop area. Another method of crop mapping uses multi-temporal MODIS imagery to classify crops using annual crop phenology for identification . These studies illustrate the ability of time series datasets to produce detailed and accurate crop classification maps at the end of an agricultural year in a single study area, but this methodology also faces challenges that hinder its practical and scientific usefulness in California. First, the spatial resolution of MODIS is not fine enough to individually classify many fields. For example, the average size of a field in the area of this study is approximately 0.2 km2 . Therefore, even at its finest resolution of 250 m, most MODIS pixels will result in mixtures of different fields or crop types, and are therefore best suited for croplands at larger scales . Second, multitemporal crop mapping is limited in its spatial scope due to a spatial variation in phenology that would decrease the accuracy if it was applied over a large spatial area . Third, the co-registration of multiples images and the need for cloud-free images create challenges for time-series analysis that single-data hyperspectral analyses do not face .

Finally, the need for multiple images throughout time obviates the ability to conduct real-time crop assessments. Hyperspectral imagery can act as a complement to these current crop-mapping initiatives, as it has the potential to identify crops at a single time point with greater accuracy than broadband sensors, and therefore can provide mid-season assessments of crop area without a yearly time-series . Discriminating crop types is challenging due to differing biophysical traits, development stages, variable management practices, regional weather and topography, and the timing of plantings . Despite these complications, various studies have successfully shown the ability to use hyperspectral imagery to classify crops and cultivars . By discriminating crop types with a single image from one time point, hyperspectral imagery can serve as a time-critical agricultural management tool, providing scientists, farm managers, and policymakers with improved information regarding the agricultural landscape and on-the-ground food and water needs. This study uses airborne hyperspectral imagery over a portion of the Central Valley to assess the accuracy of imaging spectroscopy for agricultural classifications and conducts a case study to display the utility of these classifications for analyzing changes in farming decisions. The results of this study, while limited in their spatial scope due to the use of airborne imagery, are salient in light of recently available Sentinel-2 data and the proposed HyspIRI mission, which would provide repeat, global hyperspectral imagery. In order to separate soil or fallow pixels from those of agricultural plant matter, a spectral mixture analysis was run on each of the three images to obtain fractional green vegetation cover. Multiple End member Spectra Mixture Analysis uses a linear mixture model to unmix pixels into fraction images while allowing the number and types of end members to vary on a per-pixel basis, thus better accounting for end member variability. Pixels were modeled as a mixture of green vegetation , soil, nonphotosynthetic vegetation , and shade. Image end members were chosen from each of the three images from 2013, 2014, and 2015 by selecting pixels with high overall reflectance from each of the three end member categories that were well-distributed spatially throughout the image in order to capture the variability from north to south along the flight line. A combined library of all of the chosen end members, consisting of eight NPV, 10 Soil, and 21 GV endmembers, was used for analysis in order to obtain consistent results throughout the years. MESMA was partially constrained by requiring shade fractions to vary between 0–0.8, and setting a maximum allowable root mean squared error of 0.025. The spectral mixture result was then shade normalized by dividing each non-shade component, GV, NPV, and soil by the sum total of all of the non-shade components in that pixel to obtain physically realistic fraction estimates . Only those pixels that contained 50% or more shade-normalized GV were chosen for training and validation, as this was decided as the threshold for classifying a pixel as a crop.Due to the high diversity of crop species in the Central Valley, we focused on a smaller set of crop classes that would be of the most practical use to stakeholders such as water managers, farmers, and scientists. Crops were classified into categories defined and used by the California Department of Water Resources to estimate water use . The crops within each category have similar rates of development, rooting depths, and soil characteristics, and are therefore presumed to have similar water requirements. Categories were included in the classification if they were prominent in the area, defined as ≥20 fields of that category, each of which contained ≥50% green vegetation, in the validation layers .

Posted on

The hedonic approach attempts to measure directly the effect of climate on land values

There is a growing consensus that emissions of greenhouse gases due to human activity will lead to higher temperatures and increased precipitation. It is thought that these changes in climate will impact economic well being. Since temperature and precipitation are direct inputs in agricultural production, many believe that the largest effects will be in this sector. Previous research on the benchmark doubling of atmospheric concentrations of greenhouse gases is inconclusive about the sign and magnitude of its effect on the value of US agricultural land . Most previous research employs either the production function or hedonic approach to estimate the effect of climate change.Due to its experimental design, the production function approach provides estimates of the effect of weather on the yields of specific crops that are purged of bias due to determinants of agricultural output that are beyond farmers’ control . Its disadvantage is that these experimental estimates do not account for the full range of compensatory responses to changes in weather made by profit maximizing farmers. For example in response to a change in climate, farmers may alter their use of fertilizers, change their mix of crops, or even decide to use their farmland for another activity . Since farmer adaptations are completely constrained in the production function approach, it is likely to produce estimates of climate change that are biased downwards. Its clear advantage is that if land markets are operating properly, prices will reflect the present discounted value of land rents into the infinite future. In principle, this approach accounts for the full range of farmer adaptations. The limitation is that the validity of this approach requires consistent estimation of the effect of climate on land values.

Since at least the classic Hoch and Mundlak papers,growing blueberries it has been recognized that unmeasured characteristics are an important determinant of output and land values in agricultural settings.2 Consequently, the hedonic approach may confound climate with other factors and the sign and magnitude of the resulting omitted variables bias is unknown. In light of the importance of the question, this paper proposes a new strategy to estimate the effects of climate change on the agricultural sector. We use a county-level panel data file constructed from the Censuses of Agriculture to estimate the effect of weather on agricultural profits, conditional on county and state by year fixed effects. Thus, the weather parameters are identified from the county specific deviations in weather about the county averages after adjustment for shocks common to all counties in a state. This variation is presumed to be orthogonal to unobserved determinants of agricultural profits, so it offers a possible solution to the omitted variables bias problems that appear to plague the hedonic approach. Its limitation is that farmers cannot implement the full range of adaptations in response to a single year’s weather realization, so its estimates of the impact of climate change are biased downwards. Our analysis begins with a reexamination of the evidence from the hedonic method. There are two important findings. First, the observable determinants of land prices are poorly balanced across quartiles of the long run temperature and precipitation averages. This means that functional form assumptions are important in this approach. Further, it may suggest that unobserved variables are likely to covary with climate. Second, we replicate the previous literature’s implementation of the hedonic approach and demonstrate that it produces estimates of the effect of climate change that are very sensitive to decisions about the appropriate control variables, sample and weighting.

We find that estimates of the effect of the benchmark doubling of greenhouse gasses on the value of agricultural land range from -$420 billion to $265 billion , which is an even wider range than has been noted in the previous literature. Despite its theoretical appeal, the wide variability of these estimates suggests that the hedonic method may be unreliable in this setting.The results from our preferred approach suggest that the benchmark change in climate would reduce annual agricultural profits by $2 to $4 billion, but the null effect of zero cannot be rejected. When this reduction in profits is assumed permanent and a discount rate of 5% is applied, the estimates suggest that the value of agricultural land is reduced by $40 to $80 billion, or –3% to –6%. Notably, we find modest evidence that farmers are able to undertake a limited set of adaptations in response to weather shocks. In the longer run, they can engage in a wider variety of adaptations, so our estimates are downwards biased relative to the preferred long run effect. Together the point estimates and sign of the likely bias contradict the popular view that climate change will have substantial negative effects on the US agricultural sector. In contrast to the hedonic approach, these estimates of the economic impact of global warming are robust. For example, the overall effect is virtually unchanged by adjustment for the rich set of available controls, which supports the assumption that weather fluctuations are orthogonal to other determinants of output. Further, the qualitative findings are similar whether we adjust for year fixed effects or state by year fixed effects . This finding suggests that the estimates are due to output differences, not price changes. Finally, we find substantial heterogeneity in the effect of climate change across the United States. The largest negative impacts tend to be concentrated in areas of the country where farming requires access to irrigation and fruits and vegetables are the predominant crops .

The analysis is conducted with the most detailed and comprehensive data available on agricultural production, soil quality, climate, and weather. The agricultural production data is derived from the 1978, 1982, 1987, 1992, and 1997 Censuses of Agriculture and the soil quality data comes from the National Resource Inventory data files from the same years. The climate and weather data are derived from the Parameter-elevation Regressions on Independent Slopes Model . This model generates estimates of precipitation and temperature at small geographic scales, based on observations from the more than 20,000 weather stations in the National Climatic Data Center’s Summary of the Month Cooperative Files during the 1970-1997 period. The PRISM data are used by NASA, the Weather Channel, and almost all other professional weather services. The paper proceeds as follows. Section I motivates our approach and discusses why it may be an appealing alternative to the hedonic and production function approaches. Section II describes the data sources and provides some summary statistics. Section III presents the econometric approach and Section IV describes the results. Section V assesses the magnitude of our estimates of the effect of climate change and discusses a number of important caveats to the analysis. Section VI concludes the paper. The production function approach relies on experimental evidence of the effect of temperature and precipitation on agricultural yields. The appealing feature of the experimental design is that it provides estimates of the effect of weather on the yields of specific crops that are purged of bias due to determinants of agricultural output that are beyond farmers’ control . Consequently, it is straightforward to use the results of these experiments to estimate the impacts of a given change in temperature or precipitation. Its disadvantage is that the experimental estimates are obtained in a laboratory setting and do not account for profit maximizing farmers’ compensatory responses to changes in climate. As an illustration, consider a permanent and unexpected decline in precipitation. In the short run,square plant pots farmers may respond by increasing the flow of irrigated water or altering fertilizer usage to mitigate the expected reduction in profits due to the decreased precipitation. In the medium run, farmers can choose to plant different crops that require less precipitation. And in the long run, farmers can convert their land into housing developments, golf courses, or some other purpose. Since even short run farmer adaptations are not allowed in the production function approach, it produces estimates of climate change that are downward biased. For this reason, it is sometimes referred to as the “dumb-farmer scenario.”

In an influential paper, Mendelsohn, Nordhaus, and Shaw proposed the hedonic approach as a solution to the production function’s shortcomings . The hedonic method aims to measure the effect of climate change by directly estimating the effect of temperature and precipitation on the value of agricultural land. Its appeal is that if land markets are operating properly, prices will reflect the present discounted value of land rents into the infinite future. To successfully implement the hedonic approach, it is necessary to obtain consistent estimates of the independent influence of climate on land values and this requires that all unobserved determinants of land values are orthogonal to climate.4 We demonstrate below that temperature and precipitation normals covary with soil characteristics, population density, per capita income, latitude, and elevation. This means that functional form assumptions are important in the hedonic approach and may imply that unobserved variables are likely to covary with climate. Further, recent research has found that cross sectional hedonic equations appear to be plagued by omitted variables bias in a variety of settings .5 Overall, it may be reasonable to assume that the cross-sectional hedonic approach confounds the effect of climate with other factors . This discussion highlights that for different reasons the production function and hedonic approaches are likely to produce biased estimates of the economic impact of climate change. It is impossible to know the magnitude of the biases associated with either approach and in the hedonic case even the sign is unknown. In this paper we propose an alternative strategy to estimate the effects of climate change. We use a county-level panel data file constructed from the Censuses of Agriculture to estimate the effect of weather on agricultural profits, conditional on county and state by year fixed effects. Thus, the weather parameters are identified from the county-specific deviations in weather about the county averages after adjustment for shocks common to all counties in a state. This variation is presumed to be orthogonal to unobserved determinants of agricultural profits, so it offers a possible solution to the omitted variables bias problems that appear to plague the hedonic approach. This approach differs from the hedonic one in a few key ways. First, under an additive separability assumption, its estimated parameters are purged of the influence of all unobserved time invariant factors. Second, it is not feasible to use land values as the dependent variable once the county fixed effects are included. This is because land values reflect long run averages of weather, not annual deviations from these averages, and there is no time variation in such variables. Third, although the dependent variable is not land values, our approach can be used to approximate the effect of climate change on agricultural land values. Specifically, we use the estimates of the effect of weather on profits and the benchmark estimates of a uniform 5 degree Fahrenheit increase in temperature and 8% increase in precipitation to calculate the expected change in annual profits . Since the value of land is equal to the present discounted stream of rental rates, it is straightforward to calculate the change in land values when we assume the predicted change in profits is permanent and make an assumption about the discount rate. Since climate change is a permanent phenomenon, we would like to isolate the long run change in profits. Consider the difference between the first term in equation in the short and long run in the context of a change in weather that reduces output. In the short run, supply is likely to be inelastic , which means that Short Run > 0. This increase in prices will help to mitigate farmers’ losses due to the lower production. However, the supply of agricultural goods is more elastic in the long run, so it is sensible to assume that Long Run is smaller in magnitude and perhaps even equal to zero. Consequently, the first term may be positive in the short run but small, or zero in the long run. Although our empirical approach relies on short run variation in weather, it may be feasible to abstract from the change in profits due to price changes . Recall, the price level is a function of the total quantity produced in the relevant market in a given year.

Posted on

Type-1 crops describe field and row crops that have a period of senescence and defoliation

Recent efforts aimed at establishing, standards for data quality indicators and other scoring criteria are driven in part by a desire to properly account for sources of uncertainty in life-cycle assessments . Similar desires have been expressed towards water footprint assessments. As described by Hoekstra : “The field has to mature still in terms of calibrating model results against field data, adding uncertainties to estimates and inter-model comparisons as done in the field of climate studies”. Additionally, researchers now rely on computational methods to synthesize the large quantity of environmental data and observations that are characteristic of studies conducted at large temporal or regional scales. It is still uncommon for data and computational methods to be published along with the completed studies, which obstructs the reproducibility of many hydrologic studies . These later reasons motivated the form of this study—an elementary water footprint analysis decomposed into a reproducible framework. As a case study in resource sustainability, the State of California presents a unique combination of agricultural and economic activities, resource constraints, and environmental monitoring efforts. Among the United States, California has the greatest population, greatest total farm sales , and if considered separately, would rank as the fifth largest economy in the world, by gross domestic product . Nine out of California’s one hundred million acres contain irrigated agriculture; which requires 30 million acre feet of irrigation in an average year, accounting for 80% of the state’s water use . This freshwater requirement is met in part from a vast network of water storage and conveyance infrastructure, which transfer water from the northern third of California,barley fodder system where 2/3 of the precipitation and runoff occurs, to the southern two-thirds, where 3/4 of the anthropogenic water demands are located .

Management of California’s freshwater resources are constrained by dynamic availability on one side and strong, persistent demands on the other. Seasonal variations in precipitation affect the availability of freshwater resources in California The state has recently endured a 5-year drought from 2011-2016, marked by a period from 2012 to 2014 that had the worst drought severity in the past millennium. On the other side, California’s water resources underpin its standing as one of the most productive agricultural exporters in the world and as an important component of the nation’s food security. In 2015, California produced more than 99 percent of the United States’ almonds, pistachios, walnuts, grapes, peaches, and pomegranates . In the same year, international exports accounted for approximately 26 percent of the state’s agricultural production by volume, adding up to 44 percent of the total agricultural sales by value. California is the sole national exporter of many valuable commodities, including almonds, walnuts, and pistachios, which all lie in the top five of the state’s agricultural exports by value . Unpredictable seasonal availability and uncertain international appetite makes it difficult to predict the nature of future constraints and pressures on California’s water resources. There is no guarantee that future climatic, economic, or resource environments will accommodate all of the things that societies value: healthy produce, delicious animal foods, verdant natural vistas, thriving native wildlife, and the autonomy that comes from regional food security. The current attention placed in life-cycle sustainability indicators demonstrates an awareness of the desire to maintain environmental, social, and economic systems without limiting the ability of future generations to meet their needs .

When coupled with scenario analysis, these indicators can support strategic decisions to ensure the security of natural resource supplies. Water footprint assessments have been used to quantify the impact of lifestyles on California’s water resources and have been proposed as policy support tools . Additionally, these assessments have been used to describe the effect of California water resource challenges on international trade networks . While water footprint assessments align with the resource sustainability challenges of California, water scarcity is a problem shared by many nations globally . Therefore, reproducible sustainability assessments are useful in their ability to be applied and compared between different environmental and economic systems.This study used the California Irrigation Management Information System to obtain daily reference evapotranspiration observations across the state. Specifically, the Spatial CIMIS data product was used to obtain raster representations of daily ET0 at a 4 km spatial resolution. This data was upscaled to 30 meters, using bilinear interpolation . The original data is housed and maintained by the California Department of Water Resources , and can be accessed through the CIMIS web interface. CIMIS comprises a network of over 100 automated weather stations that measure the different meteorological parameters at urban and rural sites throughout California. The system was originally established as a project of DWR and the University of California, Davis in 1982 . Each station is sited away from buildings and trees, on a bed of healthy grass that is: “well maintained, properly irrigated and fertilized and mowed or grazed frequently to maintain a height between 10 to 15 centimeters ” . Hourly weather observations are transmitted nightly to Sacramento, where the data are used to compute an average daily evapotranspiration of the reference grass surface underneath each station, using a modified version of the 1977 FAO Penman-Monteith ET0 equation .

The CIMIS Equation differs in its use of a wind function and a method of calculating net radiation from mean hourly solar radiation .The ET0 observations are made publicly available with the primary purpose of aiding agricultural growers develop irrigation schedules. While the CIMIS network provides station-specific ET0 calculations, the Spatial CIMIS data product produces a continuous daily ET0 calculation across the entire state. This is accomplished by using raster observations from the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellite system as inputs to the ASCE-Penman-Monteith ET equation . Spatial CIMIS also interpolates temperature and wind measurements from CIMIS stations, to serve as inputs to the ASCE-PM equation . Radiative inputs to the ASCE-PM equation are derived from a clear sky factor that is directly related to cloud cover, as observed by GOES satellite data. Specifically, Spatial CIMIS uses GOES visible imagery to derive a clearness parameter that is directly related to cloud cover in a given grid cell. This is combined with a clear sky solar radiation model developed for the Heliosat-II model . Heliosat-II is a software commissioned by the Solar Radiation Data project, with the purpose of converting images acquired by geostationary meteorological satellites into maps of global solar irradiation, received at ground level . The model incorporates a seasonal turbidity factor, which describes atmospheric attenuation of light due to aerosols and gases. Additional description of inputs to the Spatial CIMIS implementation of the ASCE-PM equation can be found in Appendix. Spatial CIMIS has a weakness in estimating solar radiation in scenarios where changes in the surface albedo can be mistaken for cloud cover. This typically occurs in regions that have snowfall and persistent fog,hydroponic barley fodder system both common winter conditions for some regions in California. Grid cells that contain snow cover and/or fog that persist for greater than 14 days lead to an underestimation of cloud cover and an over-prediction of net radiation during cloudy days Hart et al., 2009. Depending on the location in California, some studies have found good agreement between Spatial CIMIS ET0 and other methods, while others have used Spatial CIMIS after applying correction factors . This study used crop coefficients from Basic Irrigation Scheduling to scale Spatial CIMIS ET0 into crop-specific estimations of evapotranspiration ETc. Kc values for 45 unique crops were selected from the BIS software. These values were supplemented with Kc values from the Consumptive Use Program Plus for garlic and oranges and values from the University of California Division of Agriculture and Natural Resources for some orchard crops. Kc values for peppermint and unspecified caneberries were selected from the AgriMet crop coefficients, which were assembled by the United States Bureau of Reclamation , Pacific Northwest region. Kc values for unstressed Pomegranites were obtained from a study conducted at the Ben-Gurion University of the Negev, Israel. BIS is an application implemented in Microsoft Excel that is used for the planning of irrigation schedules for crops in California .

The software was developed as a collaboration between the University of California, Davis, the California Department of Water Resources, and the University of California Cooperative Extension. The program is currently hosted by the UC Davis Biometerology Group and can be accessed at the BIS home page. Among other uses, BIS is used to determine irrigation schedules, irrigation timings, and maximum allowable soil water depletion for 66 unique crop types. It accomplishes this by estimating crop evapotranspiration given mean climate data for a particular region. BIS partitions evapotranspiration into the component of water evaporated from spoil and plant surfaces and the component transpired by leaves . As the crop matures, the ratio of T to ET increases, until the transpiration component dominates crop ET. To account for the variable ETc , BIS defines: Kc values at different stages in a crop’s life cycle, typical planting and harvest days, and the proportion of the growing period dedicated to each growth stage. These coefficients are defined according to the FAO-56 “single crop coefficient” method, which assigns values according to 4 growth stages of a typical crop: initial growth, crop development, mid-season, and late-season . These growth stages characterize a crop’s daily Kc function, a curve that describes how the values vary as a function of the time in the crop’s growing period. BIS distinguishes between four main crop types.They are characterized by crop coefficients with three inflection points, at 10% ground shading, 75% ground shading, and the onset of senescence. Some type-1 crops such as peas and lettuce, are harvested before their period of senescence. They are characterized by two inflection points, at 10% ground shading and 75% ground shading. Type-2 crops have Kc values that are essentially fixed for most of the season. These include alfalfa, pasture, and most types of turfgrass. Shading of soil by dormant grass may cause an over prediction of soil evaporation and total ETc, however the error may be slight due to the lower overall ETo during the cold winter season Richard L. Snyder, 2014. Type-3 crops do not have a water requirement prior to shoot and leaf growth in the spring and can be characterized by a Kc curve with two inflection points. Type-4 crops represent orchard crops that have fixed Kc values throughout their growing season—similar to type-2 crops. Type-4 crops include subtropical orchards . This study assigned Kc values to individual grid cells according to the crop cover, as observed in the Cropland Data Layer . The United States Department of Agriculture National Agricultural Statistics Service has produced land cover raster image products for major agricultural regions since 1970, and for the 48 conterminous states since 2009 . Annual CDL images can be viewed through CropScape, a web GIS application maintained by USDA-NASS and the Center for Spatial Information Science and Systems at George Mason University. CDL rasters can be downloaded from the CropScape web service, or at the National Resources Conservation Service Geospatial Data Gateway. The CDL was first created by the USDA NASS Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section . It was based on an image processing and acreage estimation software named Peditor, written in the 1970s and maintained through 2006 . The stated goal of the NASS CDL program is to provide commodity acreage estimates to the Agricultural Statistics Board and other agricultural stakeholders. CDL rasters use standard land cover categories, with an emphasis on agricultural land covers. Records for the State of California begin in the 2007 calendar year; CDL products have a 56-meter spatial resolution from 2007-2009, and a 30-meter spatial resolution from 2009-present. Currently, the CDL is primary constructed from the supervised classification of remotely sensed satellite imagery, from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, RESOURCESAT-1 . This is supplemented with imagery from land imaging sensors onboard the United States Geological Survey Landsat satellites and 16-day Normalized Difference Vegetation Index composites, from the National Aeronautics and Space Administration moderate-resolution imaging spectroradiometer .

Posted on

Chemical synthesis is a widely adopted approach to generate such analogs of existing NPs

Humans have been using the potent action of NPs for multiple purposes from medicinal to cosmetic and recreational use as well as in agriculture. During the golden age of NP discovery from the 1960s to the 1980s, scientists in academia and industry identified and characterized an impressive list of NPs that are still being used today: The antibiotic compounds penicillin or amphotericin, the cholesterol-lowering lovastatin, or the cancer drug taxol are just a few examples of how these microbial ecological weapons were repurposed for combatting diseases.In agriculture, NPs have been applied as fungicides, insecticides, and herbicides that have contributed substantially to the increases of crop yield and quality worldwide. From 1997 to 2010, NPs and their derivatives made up about 36% of all new registered pesticide ingredients. For example, spinosyn and avermectin, produced by soil-borne bacteria Saccharopolyspora spinose and Streptomyces avermitilis, can effectively paralyze insects through hyperexcitation of their nervous system . The discovery of avermectin by Satoshi  Omura was awarded the 2015 Nobel Prize in Physiology or Medicine. Phosphinothricin, also known as glufosinate, produced by Streptomyces, has been commercialized by Bayer as an herbicide under the tradename of Finale . By inhibiting glutamine synthetase, glufosinate kills plants via ammonia buildup in the thylakoid lumen, which leads to decoupling of photophosphorylation. Fenpicoxamid is a commercialized fungicide derived from the NP antimycin that inhibits cellular respiration . The sales of both glufosinate and fenpicoxamid exceed US$1 billion annually. There are many other NPs with unique modes of actions that have not been commercialized owing to the high cost of mass production. For example, potential herbicides thaxtomin and tentoxin are able to disrupt cellulose biosynthesis and energy transfer, respectively; cornexistin possesses broad-spectrum herbicidal activity via inactivation of aminotransferases but only low activity against maize .

Nonetheless, new products are constantly needed: It is estimated that up to 50% of global crop yields are lost each year mainly due to pesticide resistance. Hence, there is a continuous demand to discover new insecticides,grow bucket fungicides and herbicides with novel modes of action, accompanied by efforts to decrease their production cost. Not surprisingly, NPs have remained important sources for such discovery efforts. Here, we describe how deeper understanding of NPs and their biosynthesis may lead us to new products for agricultural use.NP discovery was traditionally performed by isolating organic molecules from anorganism of interests. The workflow involves the collection or growth of the organism, followed by extraction of organic molecules and fractionation of the extract. The isolation of a pure NP from complex extracts is typically guided by screens, either via direct biological assays of the target enzyme or through identification of novel structural features. These techniques have proven to be hugely successful during the golden age of antibiotic discovery. In recent years, however, reports of new structures and activities have slowed significantly, leading many pharmaceutical companies to abandon NP programs. The advancement of new techniques in genomics has brought a renaissance to NP discovery. Thanks to the rapid development of DNA sequencing technologies, an increasing number of whole genome sequences are now available for research. Extensive studies into the biosynthesis of NPs and the genes encoding the enzymes involved have shown that the genes for one NP are typically clustered, which presumably facilitates co-regulation during transcription, and horizontal cluster transfer between species. A bio-synthetic gene cluster can be readily identified using powerful software packages through an anchoring bio-synthetic enzyme that produces the core of a NP. Such anchoring enzymes include polyketide synthases, nonribosomal peptide synthetases, or terpene synthases.The number of BGCs in a microorganisms identified in silico is therefore a reasonable estimation of the total number of NPs an organism can potentially produce. Given that only a small fraction of BGCs are associated with known compounds, the true bio-synthetic potential of microbes is much larger than the number of known NPs.

Indeed, most BGCs remain silent owing to their complex regulation and our inability to reproduce the natural environmental cues that are needed to turn them on: It is estimated that more than 90% of BGCs remain as genomic “dark matter” encoding secondary metabolites that have eluded traditional NP discovery. It is therefore tantalizing to speculate how many new NPs could be discovered if we can efficiently tap into these silent BGCs. Different approaches have been applied to awaken these gene clusters, including constitutively expressing pathway-specific transcription factors, epigenetic modifications to alter chromatin structure and transcriptional activities, and heterologous expression of desired pathways in model hosts. While these approaches are successful in inducing BGCs to produce new NPs, their true biological activities are typically unknown: Compared to more traditional NP discovery, the genomic approaches are not activity-guided. Giventhe large number of BGCs available, it is essential to prioritize genome-driven discovery of NPs by biological activity. How can we predict the activity of a NP based on genomic sequence? The answer to this question can unlock the true untapped potential of the tens of thousands of BGCs.To find agriculturally useful NPs with new modes of action from tens of thousands predicted BGCs, we developed a resistance gene-guided approach. The rationale is that host organisms producing NPs that target housekeeping enzymes must have a method of protecting themselves. Several mechanisms of self-resistance are known: efflux pumps that actively transport the metabolite to the extracellular space; proteins that stoichiometrically bind to the NPs; and enzymes that modify the housekeeping target to evade NPs. Nature also evolved the clever strategy to encode a mutated copy of the sensitive housekeeping gene in the NP BGC . This self-resistance enzyme can carry out the same function as the housekeeping enzyme, but is sufficiently mutated to be insensitive to the NP. Because the self-resistance gene is required for survival during NP production, it is frequently co-localized in the same BGC. An example is the lovastatin BGC: A second copy of 3-hydroxy-3-methylglutaryl-coenzyme A reductase , which is the target of lovastatin, is encoded in the lovastatin BGC in Aspergillus terreus . This co-localization has been exploited to link BGCs to compounds with known targets. We propose that using the self-resistance gene as a predictive marker, one can mine NPs from collections of BGCs with desired bio-activity. A workflow for such guided genome mining looks as follows: After identifying a desired target enzyme that is also present in microorganisms, one can search through genome databases for BGC carrying duplicate copies of the target gene that is located close to a bio-synthetic anchoring enzyme; different synthetic biology approaches can be applied to produce the NP encoded in the cluster; the NP is isolated and the structure is elucidated using NMR spectroscopy; and inhibition of the housekeeping enzyme and insensitivity toward the self-resistance enzyme are validated biochemically or genetically. We applied this approach to search for herbicide leads with novel modes of action to target dihydroxy acid dehydratase within the branched-chain amino-acid bio-synthetic pathway. We first scanned fungal genomes in publicly available databases for a BGC that encodes a possible resistant copy of DHAD. We eventually found a conserved four-gene cluster in Aspergillus terreus, which encodes a terpene synthase, two cytochrome P450s and a duplicate copy of DHAD that is about 60% identical to the well-conserved housekeeping DHAD. The cluster was introduced into Saccharomyces cerevisiae, which produced aspterric acid at 20 mg/l. Consistent with our hypothesis,dutch bucket for tomatoes aspterric acid was verified as a potent competitive inhibitor of the housekeeping DHAD enzyme from A. terreus and Arabidopsis thaliana. In contrast, the Aspergillus self-resistance DHAD was insensitive to aspterric acid.When applied in planta, aspterric acid showed strong growth inhibition of representative monocots and dicots.

When applied at lower concentration, aspterric acid could specifically inhibit the formation of pollen without harming pistil development. Hence, aspterric acid may be used as a chemical hybridization agent in the field to facilitate out cross and hybrid seed production. Motivated by the success of using a combination of glyphosate-based herbicide and glyphosate-tolerant crops in weed control, we also demonstrated the possibility of creating transgenic plants that can tolerate aspterric acid treatment through expression of the resistance gene astD.Discovery and proof of action is just one part of the process though; for using NPs in agriculture also requires large-scale and cost effective production. Microbial fermentation using genetically modified organisms has therefore great potential to produce a given compound at high titer. Furthermore, the use of a generally regarded as safe organisms such as S. cerevisiae as a production host can alleviate public concerns. In recent years, S. cerevisiae has been intensely pursued as a host for production of biofuels, chemicals, and pharmaceuticals, the last of which mostly consist of NPs. For example, a 23-step biosynthesis of opioids was recently achieved by combining enzymes from different organisms into a bio-synthetic pathway in yeast. Similarly, strictosidine, the common precursor to thousands of plant monoterpene indole alkaloid NPs, can now be produced from yeast after introducing more than twenty genetic changes. NPs are secondary metabolites that are synthesized from primary metabolites as building blocks. For example, aspterric acid is a sesquiterpene, which is synthesized from five-carbon isoprenoid building blocks that are universally used in terpene biosynthesis. The isoprenoid building blocks, IPP and DMAPP, are in turn synthesized from acetyl-CoA, a central metabolite in aerobic respiration, fatty acid biosynthesis, and protein acetylation. Thus, the yield of terpene-derived NPs can be increased through engineering of the host primary metabolism to elevate acetyl-CoA concentrations.In recent years, metabolic engineering and synthetic biology have turned Baker’s yeast into efficient microbial factories. Metabolic engineering approaches, such as ove rexpression of pathway genes to produce NPs, or increasing flux of building blocks, such as acetyl-CoA, can lead to dramatic increases in target compound titers. One milestone example is the production of the plant metabolite artemisinic acid at titers of 25 g/l from yeast, as a sustainable source of the antimalarial compound artemisinin. Artemisinic acid, like aspterric acid, is an oxidized sesquiterpene synthesized by the collaborative action of terpene synthase and a P450 monooxygenase. It is therefore reasonable to expect there is ample room to further increase the titers of aspterric acid from the current levels of ~20 mg/l. Moreover, gene-editing tools have further revolutionized our capability to genetically engineer microorganisms. A suite of recently developed CRISPR-based strain evolution strategies are promising multiplex tools for strain engineering. Other synthetic biology tool kits for yeast, including product compartmentalization and enzyme prospecting, as well as directed evolution of bottleneck enzymes, will help to further increase NP titers.Although some NPs can be directly used in agriculture or medicine, most compounds require further modification to improve their biological activity. A diversified library of NPs can help to illuminate the structure–activity relationships of the compound and allow screening of analogs that are more potent, or that can overcome evolved resistance mechanisms.However, many NP structures are difficult to manipulate chemically and often degrade quickly if chemists try to modify them. Moreover, chemically modified compounds are no longer considered “natural” and can face significant regulatory hurdles. Therefore, engineering of the bio-synthetic pathway to create structural analogs is an attractive alternative to chemical modification or synthesis. This requires a thorough understanding of the microbial NPs bio-synthetic machinery, including the sequence of enzymatic transformations, the mechanisms and substrate flexibilities of individual enzymes. NP biosynthesis usually follows a “linear” sequence of enzymatically catalyzed reactions, reflecting nature’s bio-synthetic logic in constructing a complex molecule. Some of these enzymes are promiscuous and can function out of sequence or use alternative substrates. This allows synthetic biologists to exploit the bio-synthetic machineries for the synthesis of many NPs to produce “unnatural” NPs. One effective method to expand NP structural diversity is through precursor-directed biosynthesis and mutasynthesis . In contrast, de novo biosynthesis of diversified NP molecules can be achieved by mixing and matching enzymes from different bio-synthetic pathways . Combinatorial biosynthesis, which parallels the concept of combinatorial synthesis, is particular successful in bio-synthetic pathways that utilize modular enzymes such as polyketide synthases and nonribosomal peptide synthetases . Individual domains of these “assembly-line” enzymatic machineries canbe inactivated, inserted, or swapped to precisely introduce modifications to the final NP structure.

Posted on

Environmental pressures will further limit the possibility for land expansions

The mean annual precipitation is below 250 mm in about 70% of the country and only 3% of Iran, i.e. 4.7 million ha, receives above 500 mm yr−1 precipitation . The geographical distribution of Iran’s croplands shows that the majority of Iran’s cropping activities take place in the west, northwest, and northern parts of the country where annual precipitation exceeds 250 mm . However, irrigated cropping is practiced in regions with precipitations as low as 200 mm year−1 , or even below 100 mm year−1 . To support agriculture, irrigated farming has been implemented unbridled, which has devastated the water scarcity problem. challenges: providing domestic food to a rapidly growing population on a thirsty land.When land suitability was evaluated solely based on the soil and topographic constraints , 120 million ha of land was found to have a poor or lower suitability ranks . Lands with a medium suitability cover 17.2 million ha whilst high-quality lands do not exceed 5.8 million ha . The spatial distribution of suitability classes shows that the vast majority of lands in the center, east and, southeast of Iran have a low potential for agriculture irrespective of water availability and other climate variables . As shown in Fig. 2, the potential agricultural productivity in these regions is mainly constrained by the low amount of organic carbon and high levels of sodium contents . Based on soil data, Iran’s soil is poor in organic matters with 67% of the land area estimated to have less than 1% OC. Saline soils, defined by FAO as soils with electrical conductivity >4 dS/m and pH<8.2, are common in 41 million ha of Iran. Although many plants are adversely affected by the saline soils , there are tolerant crops such as barley and sugar beet that can grow almost satisfactorily in soils with ECs as high as 20 dS/m,nft growing system which was used as the upper limit of EC in this analysis .

Although sodic soils are less abundant in Iran , soils that only have high ESP covers ~30 million ha . We used an ESP of 45% as the upper limit for cropping since tolerant crops such as sugar beet and olive can produce acceptable yield at such high ESP levels. As shown in Fig. 2, EC is not listed among the limiting factors, while we know soil salinity is a major issue for agriculture in Iran. This discrepancy can be explained by the higher prevalence of soils with ESP>45% compared to those with EC>20 dS/m, which can spatially mask saline soils. That is, the total area of soils with EC>20 dS/m was estimated to be about 6.4 million ha , while soils exceeding the ESP threshold of 45 constituted ~12 million ha i.e. almost double the size of saline soils. Iran’s high-quality lands for cropping are confined to a narrow strip along the Caspian Sea and few other provinces in the west and northwest . In the latter provinces, the main agricultural limitations are caused by high altitude and steep slopes as these regions intersect with the two major mountain ranges in the north and west .Thus far, the land suitability analysis was based on soil and terrain conditions only, reflecting the potential agricultural productivity of Iran’s without including additional limitations imposed by the water availability and climatic variables. However, Iran is located in one of the driest areas of the world where water scarcity is recognized as the main constraint for agricultural production. Based on aridity index , our analysis showed that 98% of Iran could be classified as hyper-arid, arid, or semi-arid . August and January are the driest and wettest months in Iran, respectively, as shown in Fig. 3. Over half of the country experiences hyper-arid climate conditions for five successive months starting from June . This temporal pattern of aridity index has dire consequences for summer grown crops as the amount of available water and/or the rate of water uptake by the crop may not meet the atmospheric evaporative demand during these months, giving rise to very low yields or total crop failure. It must be noted that the high ratio of precipitation to potential evapotranspiration in humid regions could also result from low temperature rather than high precipitation.

There is a high degree of overlap between regions that experience wetter conditions for most of the year and those identified as suitable for agriculture based on their soil and terrain conditions . This spatial overlap suggests that some of the land features relevant to cropping might be correlated with the climate parameters. In fact, soil organic carbon has been found to be positively correlated with precipitation in several studies. To incorporate climate variables into our land suitability analysis, we used monthly precipitation and PET as measures of both overall availability and temporal variability of water. We derived, from monthly precipitation and PET data, the length of the growing period across Iran . Growing period was defined as the number of consecutive months wherein precipitation exceeds half the PET. As shown in Fig. 3, areas where moisture conditions allow a prolonged growing period are predominately situated in the northern, northwestern, and western Iran with Gilan province exhibiting the longest growing period of 9 months. For over 50% of the lands in Iran, the length of the moist growing period is too short to support any cropping unless additional water is provided through irrigation . However, these areas, located in the central, eastern, and southeastern Iran, suffer from the shortage of surface and groundwater resources to support irrigated farming in a sustainable manner. Taking into account daily climate data and all types of locally available water resources can improve the accuracy of the length of growing period estimation. The productivity of rainfed farming is also affected by the selection of planting date, which often depends on the timing of the first effective rainfall events. For this joint soil-terrain-climate analysis, all regions with a growing season of two months or shorter were assigned a suitability value of zero and thus classified as unsuitable for agriculture. We then evaluated the capacity of land for rainfed farming by using a precipitation cut-of of 250 mm year−1 ,vertical hydroponic nft system which is often regarded as the minimum threshold for the rainfed farming . As shown in Table 1, the inclusion of the length of growing period and precipitation threshold into the analysis only slightly reduced the total area of high-quality lands from 5.8 to 5.4 million ha. This implies that most lands with suitable soil and terrain conditions also receive sufficient amount of moisture to sustain rainfed agriculture.

On the contrary, the area of unsuitable lands increased from 39.7 to 112.9 million ha when precipitation and duration of growing season thresholds were superimposed on the soil and topographic constraints. This increase in unsuitable acreage was mainly driven by the demotion of lands from the very poor class to the unsuitable class . The addition of moisture constraints also reduced the area of medium suitability lands by 4.8 million ha. In summary, for the rainfed farming suitability analysis, 125 million ha of Iran’s land might be classifed as poor or lower ranks whilst only 18 million ha meet the required conditions for the medium or higher suitability classes . Te geographical distribution of these land classes is mapped in Fig. 4. Almost the entire central Iran , and the vast majority of land area in the eastern , southeastern and southern provinces were found to be unsuitable for rainfed farming. Almost half the area of Khuzestan and three-quarters of Fars provinces were also characterized unsuitable. Over the entire east, only in the northern part of Khorasan Razavi province, is there a belt of marginally suitable lands satisfying the requirements of a potentially prosperous rainfed agriculture .In the next step of the analysis, the suitability of land was scaled with the annual precipitation over the range of 100 to 500 mm year−1 . The lower limit is deemed to exclude the desert areas for agricultural use whilst the upper limit represents a benign moisture environment for the growth of many crops . This last analysis, here after referred to as precipitation scaling method, makes no assumption as to whether the cropping practices rely on rainfall or irrigation to satisfy crop water requirement and may thus represent a more comprehensive approach for agricultural suitability assessment. The same minimum length of growing period and soil/topographic constraints as with the two previous methods were used in this analysis. Compared to the rainfed agriculture analysis, the precipitation scaling method mainly changed the distribution of lands within the lower suitability classes . For example, a great proportion of lands within the unsuitable class was shifted up to the very poor and poor classes. This implies that, to a limited extent, irrigation can compensate for the below threshold precipitation . Nevertheless, water availability cannot necessarily justify agriculture in areas with low soil and topographic suitability. This has an important implication for water management in Iran that has a proven record of strong desire for making water available to drier areas through groundwater pumping, water transfer, and dam construction. The majority of high-quality lands , which also retains sufficient levels of moisture are located in the western and northern provinces of Iran . Kermanshah province accommodates the largest area of such lands followed by Kurdistan .

High-quality lands were estimated to cover 33% and 21% of these two provinces, respectively. Other provinces with high percentages of high quality lands were Gilan , Mazandaran , West Azerbaijan , and Lorestan . For 17 provinces, however, high-quality lands covered less than 1% of their total area .To estimate the total area of croplands within each suitability class, we visually inspected 1.2 million ha of Iran’s land by randomly sampling images from Google Earth . The proportion of land used for cropping increased almost linearly with the suitability values obtained from the precipitation scaling method . Total cropping area in Iran was estimated to be about 24.6 million ha, which is greater than the reported value by the Iran’s Ministry of Agriculture. This authority reports the harvested area; hence, the fallow or abandoned lands are not included in their calculation of active agricultural area. Our visual method, however, captures all lands that are currently under cultivation or had been used for cropping in the near past that are now in fallow or set-aside . The relative distribution of croplands amongst the suitability classes shows that about 52% of the croplands in Iran are located in areas with poor suitability or lower ranks as identified by the precipitation scaling method. Particularly concerning are the 4.2 million ha of lands that fall within the unsuitable class. Approximately 3.4 million ha of cropping areas occur in good and very good lands . However, no agricultural expansion can be practiced in these areas as all available lands in these suitability classes have already been fully exploited. Medium quality lands comprise 12.8 million ha of Iran’s land surface area , of which about 8.6 million ha have been already allocated to agriculture . Nevertheless, due to their sparse spatial distribution and lack of proper access, only a small portion of the unused lands with medium suitability can be practically deployed for agriculture. Using FAO’s spatial data on rainfed wheat yield in Iran, we estimated the mean yield for wheat cropping areas located within each of the six suitability classes. As shown in Fig. 7, the yield of the rainfed wheat increased proportionally with improving suitability index, showing that our suitability index adequately translates to crop yield. Using the observed yield-suitability relationship , we estimated that 0.8 million ton of wheat grain might be produced per year by allocating 1 million ha of the unused lands from the medium suitability class to rainfed wheat cropping.Whilst the insufficiency of water resources has long been realized as a major impediment to developing a productive agriculture in Iran, our study highlights the additional limitations caused by the paucity of suitable land resources.That is, Iran as a member of Convention on Biological Diversity is obliged to fulfil Aichi Biodiversity Targets whose Target 11 requires Iran to expand its protected area to 17% by 2020, which is almost double the size of the current protected areas in Iran .

Posted on

Institutional barriers also constrain producers from moving into individual farming

The overall objectives of our proposed paper is to: systematically document the post-reform trends in agricultural performance in Asia, Europe, and the Former Soviet Union; identify the main reform strategies and institutional innovations that have contributed to the successes and failures of the sector; analyze the mechanisms by which reform policies and initial conditions have affected the transition process in agriculture; and draw lessons and policy implications from the experiment and identify the gaps in our understanding of the role and performance of agriculture in transition. As part of this effort, we attempt to address a number of intriguing and important questions on the performance of individual countries or regions during transition. Why has China been so successful in its reforms, while Russia has not? Why is it that some CEECs have rebounded and showing robust productivity growth, while others have not? Why has agriculture in so many FSU nations continued to perform so poorly? In addition, we will address questions about the process of reform. Why has land restitution predominated in Europe but not in Russia or China? Why did institutions of exchange collapse in the non-Asian economies in the early stages of reform but continued to function in Vietnam and China? What explains the apparent divergence in the performance effects after the first year of reform in China and Vietnam, on the one hand, and much of the rest of the transitional world on the other? In particular, how have land reform and rural input-supply/ procurement enterprise restructuring affected productivity? Which institutions of exchange and contracting have or have not emerged, and why? How has the structure of the economy at the outset of transition, and other initial conditions, affected the transition process? To meet our objectives and answer some of the questions,stacking pots we will begin by laying out the record on performance — examining the main bodies of data that demonstrate the changes in agricultural output, income, and productivity in the years after transition.

In doing so, we will show how some of the countries have recorded similar performances, while others have developed quite differently. We will identify several “patterns of transition” based on these performance indicators and much of our subsequent discussion will analyze the success of transition according to these classifications. Next, as the first step in our search for answers as to what explains these different patterns, we examine differences in the points of departure of the transition countries as well as the nature of the policy reforms that have affected agriculture. The initial conditions that we hypothesize may explain part of the transition period’s performance include the nature of agricultural technology at the beginning of the reforms , the structure of the economy , the extent of collectivization, and the magnitude of trade distortions. The key policy interventions that we should expect to affect agriculture’s performance during transition include land right reforms and farm restructuring; price and subsidization policies; the approach to the liberalization of agricultural commodity and input markets; general macro-economic and general institutional reforms; and the attention of sectoral leaders to the level of new and maintenance-oriented public goods investment . After documenting the dramatic differences in initial conditions and in reform policies among the transitional countries, we seek to demonstrate which of the differences determine the path a country’s agriculture takes. In other words, we offer answers to the question why transition in agriculture in some countries has been successful and not in others. Here, we seek to generalize about the main causes for differences between the countries and the mechanisms that have affected performance. In particular, we argue that the debate on the optimality of Big-Bang versus gradualism oversimplifies the reform problem. The empirical evidence suggests that the road to a successful transition is more subtle and successful transitions in Asia and Europe have elements of both gradual and radical reforms.

To explain the reform successes and failures we emphasize the role of the political environment in the early reform years and the potential for agricultural growth that exists at the start of reforms. We find that both have not only influenced the choice of the reform policies, but also the effect of the reform policies. We also conclude that the initial level of price distortions and the pace of market liberalization were especially influential in explaining differences in the early stages of transition but that the influence of the factors has diminished over time. Investment, land rights, and farm restructuring policies, in contrast, are assuming a more important role as the agricultural reforms have matured.In the last section we draw policy implications and lessons from the agricultural transition experiences. We argue that one should be careful about which indicator to use for measuring success and failure of transition. We conclude that all reform strategies in order to be successful need to include some certain policy ingredients . However, a powerful lesson is that although all the pieces are ultimately needed, there is a lot of room for variation in the form of institutions that can be successful, and optimal policies and institutions may vary according to initial conditions. In other words, there is no single optimal transition path. Whatever the reason—either initial conditions, reform policies, or both—remarkable differences can be observed when examining the performance of agriculture in the transitional countries during the first decade of reform . From the start of the reforms, output increased rapidly in China. After 10 years output had increased by 60 percent. In Vietnam, output also rose sharply, increasing by nearly 40 percent during the first decade of reform.Output trends followed a different set of contours outside of Asia. Production fell sharply in the first 5 years of transition in both the CEECs and in the FSU countries. Since the mid-1990s, output stabilized in most of the CEECs. In Russia and Ukraine, however, the fall continued declining to nearly 50 percent of pre-reform output. Productivity trends, while similar to those of output in certain countries, diverged in others . For example, for the entire reform period, labor productivity in the agricultural sectors of China and Vietnam, measured as output per farm worker, rose steadily like output. The productivity trends for Russia and Ukraine also mirror those of the nation’s output: labor productivity fell over 30 percent between 1990 and 1999. Productivity trends for some CEECs, however, differ from those of output. For example, output per worker almost doubled over the first decade after transition in Hungary.

Labor productivity also rose strongly in the Czech Republic and Slovakia in the 1990s, even as output was falling. While reliable estimates on total factor productivity are scarcer, the general picture is similar as the one described by the labor productivity trends. In China and Vietnam, TFP rose during the reform era . In several CEECs, TFP in crop production started increasing early on in transition . What has been behind the observed trends? To the extent that we can better understand the sources of growth, decline, and recovery, we may be able to more precisely predict what is in store for the future and derive more accurate policy implications. We start by examining initial conditions,grow lights since they may affect how a country proceeds after a change. Next, we examine the impact of policy actions taken by reforms: the record on property rights, price and subsidy policies, and a large number of measures that can be labeled as actions taken to promote the emergence of institutions of exchange, including markets. The final subsection briefly examines the record of countries in the management of agricultural investment. Although comparisons of economies in transition are reasonable, given their common reliance on central planning and shared transition era goals of liberalization and faster growth, differences in initial conditions at the outset of reform may temper comparisons. In general, the Asian economies had a much lower levels of development than the transition countries in Europe. For example, the share of agriculture in employment was more than 70% in China and Vietnam. In contrast, less than 20 percent of the working population in Russia and most of the CEECs is employed in agriculture. The demographic structure of the countries also affects the way output is produced. Farms in China and Vietnam are much more labor-intensive. The man/land ratio was more than five times higher in Asia than in Central Europe or Russia . The length of time under collectivized agriculture also may affect transition. Although pre-transition agriculture was characterized by the dominance of large-scale farms in almost all the countries,the collectivization of agriculture occurred early this century in Russia, while only after the second World War in the CEECs and East Asia. Experience with private farming and any understanding of markets was more likely completely lost during several generations under Communism in most of the FSU nations. In contrast, private farming survived in rural households in many other countries.Land ownership prior to reform also differed among the countries. In China, the collective retained legal and effective property rights both before and after the implementation of HRS.

In Russia and other FSU countries, however, land was nationalized during Communism. In many CEECs much of the collective farm land was still legally owned by individuals, although effective property rights were controlled by the state or the collective farms . Paradoxically, while these legal differences probably had little impact on the operation of the land in the various countries in the pre-reform era, they had a much stronger effect on land reforms afterward liberalization. In particular, pre-reform ownership can be quite closely linked to the demand for land restitution in the CEECs . Finally, pre-reform tax, subsidy and trade policies differed significantly among the countries. In China and Vietnam, authorities heavily taxed agriculture . In contrast, leaders in most of the CEECs and the FSU nations supported agriculture with heavy subsidies . Moreover, while some of the taxes and subsidies were direct, some differences in rates of taxation and subsidy were related to trade policies. Trade policies also affect the degree of access that consumers and producers have to world markets and how much producers are subject to global competition. For example, FSU countries were strongly integrated into the CMEA system, and traded mainly with other communist countries. The share of CMEA exports as a percent of GDP amounted to around 30 percent in Russia and Ukraine. The CEECs also traded with other countries, but CMEA exports still made up around 10 percent of GDP in countries like Hungary and the Czech Republic. In contrast, China and Vietnam mainly traded with nonCMEA countries.The reforms in China and Vietnam started with radical decollectivization and reshuffling of property rights. Reformers in China re-allocated land rights from the communes, brigades and teams to rural households and completely broke up the larger collective farms into small-scale household farms. The resulting changes in incentives triggered both strong growth of output and a dramatic increase in productivity . Doi Moi, Vietnam’s reform program in the 1980s closely followed China’s strategy and land reform also positively affected the nation’s agricultural output . In contrast, many large-scale farm organizations survived the transition in the FSU and the CEECs. Large-scale farms, under a variety of legal organizations, still cultivated more than 75% of the land in Russia, Ukraine, most of the FSU nations, and a number of CEECs five years after the start of the reforms. The break-up of the former collective and state farms into individual farms has been strongest in countries in which the collective and state farms were least efficient and most labor intensive . Importantly, the shift also was higher in regions where at least some private farming survived during Communist rule. Although the share farmed by large corporate farms has fallen gradually over the past decade in most transition countries, it is a slow process and it is not obvious that they will disappear in the near future. In some countries, such as Russia and Slovakia, policies still heavily favor large corporate farms.The corporate farms also may be providing services that provide up- and downstream activities substituting for missing markets . In many countries, such as Hungary and Bulgaria, a dual farm structure is emerging with some large-scale farms and many small-scale individual farms .

Posted on

Increased growth in response to CO2 fertilization is-well documented for many plant species

Sustainable land use is identified by most stakeholders as a priority for California, i.e., that trade offs between agricultural productivity, environmental quality, and human livelihoods and well-being be assessed for the greatest long-term benefits to society as a whole. A major risk is that sustainability may be lost when climate change and urbanization increase the pressure for short-term financial gain from current agricultural lands, especially given a range of potential scenarios for climate change range between positive to problematic. For this reason, alternate coping strategies must be assessed for their short- and long-term feasibility and sustainability. The immense breadth of commodities produced in California requires that the government expand its focus on policies or programs that support the many aspects of Californian agriculture that may be affected by these changes . Crop insurance premiums will undoubtedly rise for farmers if the insurance industry perceives a threat from climate change in the form of extreme events, such as Hurricane Katrina in New Orleans, 2005. At present, practical implications for agriculture are lagging behind the science that is predicting climate change. As pointed out by the World Meteorological Organization , neither farmers nor policy makers have good access to information for decision-making, beyond that offered by general climate forecasts. This is particularly important for repercussions of land use change that will result from the combined effects of urbanization and climate change. Although technological advances have great potential for adaptation , they should be more clearly specified by joint efforts between agriculturalists and economists, so that land use changes are planned rather than reactionary to surprise events. The practicality of moving crops from one area to another area is not simple . Shifts in land-use are not considered a market impact and therefore, are not included in most global models , but they potentially have large economic and environmental effects on people and the resource base in agricultural landscapes. For this reason,hydroponic nft a cautiously optimistic approach would emphasize agricultural research and land use planning that would examine novel scenarios for agriculture to minimize risks, facilitate coping strategies for extreme events, and ensure long-term productivity, perhaps at the expense of short-term financial gains by agricultural producers or urban developers.

The potential impacts of climate change are varied, multifarious and occur across a range of temporal and spatial scales. California is a highly populated state, rapidly growing, with dwindling resources already subject to extensive competition. In the previous sections, though we organized our discussion of climate change impacts into specific categories, it was already evident that many issues crossed over the different categories. In this section, we synthesize some of the issues identified above to demonstrate the interdependence and chain effects associated with different aspects of climate change. by developing several targeted examples of climate change impacts on California agricultural landscapes, as identified in the preceding sections of this report. There are and will be other such interactions, many of which are not yet apparent.Users of agricultural water in the Central Valley are among those most vulnerable to climate change and could be devastated by severely dry forms of climate warming . The allocation of water resources across the state is in part based upon estimates of crop water use efficiency from a limited number of crop species . Urbanization of the Central Valley will place increasing pressure on water resources and reduce their availability to agriculture. Farmers are more likely to be impacted than urban and industrial users, who can pay more for water. Farmers may benefit, however, if climate change results in an increase in water availability at critical times . At present, agriculture represents approximately 7.4% of total Californian employment; however, in the Central Valley it accounts for 25% . Farming is already a precarious occupation for some and challenging resource limitations may be all it takes for some to give into urbanization pressures and sell to developers. The confluence of changing availability of water resources, increasing urbanization, and the high dependence upon agriculture as a source of employment, may lead to disproportionately large effects of climate change upon the Central Valley of California.Increased photo assimilation of C can lead to decreased concentrations of leaf N, soluble protein, and of the carboxylating enzyme, Rubisco, and nitrate reduction may be inhibited at high CO2 concentrations, such that growth is reduced. A reduction in protein and nutrient content of plant tissue may decrease the nutritive value of food for all consumers, including herbivorous pest invertebrate species .

While warming accelerates the life cycles of many invertebrates, and thus negative impacts associated with invertebrate pests , herbivorous invertebrates may actually grow more slowly because their food source is nutrient- and protein-poor. In response, these pests may increase their feeding rates to satisfy their nutritional requirements. Furthermore, decreased plant nutritional status actually decreases resistance of some plants to pathogenic organisms. These examples highlight the importance of exploring multiple effects of elevated atmospheric CO2 concentrations on crop growth and pest communities.Temperature influences key developmental stages of many important tree crops , for which California is the country’s sole producer . Decreased chilling can result in late or straggled bloom, decreased fruit set and poor fruit quality . Heat waves may also cause early bolting, or reduce pollination success. Climate warming may lead to faster developmental rates, decreased generation times, and range expansion of some pest invertebrate species . Thus, climate change may have implications for integrated pest management and control of such pests, their natural enemies, control measure and the future climate. In a warmer climate, whereas development of some tree crop species may be slowed, that of their pests may be increased, making these crops highly vulnerable to pest damage. Rapid rates of adaptation to climate change by invertebrates may exceed the slow rate of development of resistant germplasm available to growers, thus further exacerbating this situation.Soil organic matter is an important source of nutrients, especially in organically managed agroecosystems. Under a warming climate the rate of soil organic matter decomposition is predicted to increase . This may lead to enhanced nutrient availability to plants, provided nutrient release and plant demand are temporally synchronous, but may also reduce the efficacy of soil C sequestration . Soil moisture is another key driver of soil organic matter decomposition , whose availability with climate change remains hard to predict. If carbon trading markets develop in California, trade offs between enhanced nutrient supply and decreased carbon sequestration may become significant, especially given the high energy requirements for producing inorganic fertilizers.Beneficial organisms and their processes, e.g., N fixation by symbiotic and free-living rhizobia, are stimulated by elevated CO2. Conversely, ozone exposure reduces plant growth and crop yields, hinders nitrogen-fixation, compromises disease resistance, and increases susceptibility to invertebrate damage. Although ozone is phytotoxic, elevated atmospheric concentrations of CO2 can ameliorate damage caused by O3 in some circumstances. The interacting effect of different climate factors on multi-trophic interactions are uncertain, making species-specific predictions based on single-factor analyses tenuous at best. Ecosystem-context, especially on-farm or in situ studies, and experiments in changing climate scenarios are required.While by no means exhaustive,hydroponic channel the examples developed above are intended to act as stimuli for future research to identify linkages both within and beyond agriculture to understand climate change impacts and plan adaptive strategies.Impacts of climate change, irrespective of scale, land use and sector, will be wide ranging and varied.

Climate change will impact California differently than it will other parts of the United States. National policies may not always be entirely appropriate, easily implemented, or in the best interests of the state. Consequently, impacts and our response must be assessed in the context of climate change impacts and responses both within the US and globally. Furthermore, climate change and its impacts need to be taken in the context of a world that is rapidly changing in many ways. Population growth, urbanization, and shifting patterns of agricultural production, decreased water resource supply and increased competition for those resources are areas of high priority. Recognition of the fact that actions taken now and in the near future will play a critical role in mitigating and minimizing impacts, as well as maintaining flexibility and adaptive capacity, is essential. California agriculture faces serous challenges in the coming century and beyond. Be that as it may, it has shown considerable adaptive capacity in the past, and with the right information and a suitable policy environment and infrastructure, it can continue to do so into the future. California agriculture’s potential as a net mitigator of climate change is substantial, and as such is an avenue worthy of detailed investigation. Impacts of action and inaction in limiting and/or responding to climate change will be felt well into the future. The climate is changing. California agriculture stands to be impacted substantially. The time to act, with well informed, flexible and sustainable approaches, is now.Technological innovation has been identified as one of the important engines for economic development and growth . It is driven through producing knowledge by firms and individuals, which allows them to stay competitive in the market . Since the seminal paper by Griliches , the concept of the knowledge production function has been further developed in theory and applied at national , regional , sectoral , levels, and even using a meta analysis of 15 individual studies . Agriculture is one of the sectors in which innovation has become extremely important due to scarcity of natural resources, such as land and water, and increased demand for food driven by population growth. According to Food and Agricultural Organization of the United Nations estimates,global population is expected to grow by more than a third, or 2.3 billion people, between 2009 and 2050. Agricultural productivity would have to increase by about 70% to feed the global population of 9.1 billion people over this period. Arable land would need to increase by 70 million ha, with considerable pressure on renewable water resources for irrigation. Efficiency in agricultural practices and resource usage are among the suggested prescriptions to ensure sustainable agricultural production. Sands et al. also predicted net positive improvements in global agricultural production in the year 2050, in a simulated scenario of rising population and low agricultural productivity growth. While such studies are reassuring, it becomes imperative to guarantee continuous research and development in agriculture to sustain the current rate of productivity growth, and to increase it to counter both population growth and natural resource scarcity in the future. Such objectives can be met by proper investment in agricultural R&D and its dissemination to the agricultural producers. A first step is the identification of the process of converting research and dissemination inputs into knowledge used for improvement of food production. Much of the literature reviewed in Section 2 below focuses on knowledge production functions in industrial firms and sectors. Fewer works apply the concept of knowledge production function to agricultural research , and we are not aware of estimation of such function for agricultural extension. Agricultural extension is a public based research and dissemination of knowledge to farmers by universities and/or government agencies. In this paper, we apply the concept of knowledge production function to an agricultural extension system by focusing on research-based agricultural knowledge generated by the University of California Cooperative Extension . This publicly-funded research and extension system has offices across counties within the state of California. We analyze the nature of the input-output relationship between the research inputs invested by UCCE in R&D and outreach, and the knowledge produced and disseminated by UCCE. This paper contributes to the literature in several ways that set it apart from similar endeavors. To our knowledge, this paper is the first to develop a knowledge production function for an agricultural extension system that creates and disseminates knowledge, which is in itself an innovation. Second, it develops a weighted average value of knowledge, including a number of different components of knowledge produced. Third, the paper uses academic publications to measure knowledge produced by extension, as opposed to patents used in measuring knowledge in private sector. Finally, it distinguishes knowledge production across California counties and over time, suggesting relative advantages in knowledge creation by counties with potential implications for public budget allocation.

Posted on

Climate factors that affect microbial diseases are multifarious and multiplicative

Changing pest dynamics as a result of changing atmospheric conditions are of ecological and economic importance . While little is known about the direct effects of changing precipitation patterns upon invertebrates, it is known that increased rainfall can increase insect mortality . Information on direct effects of elevated atmospheric concentrations of CO2 on insects is limited , as are studies of the consequences of changing UVB levels on insect herbivores and other invertebrates. Existing studies suggest that direct effects of temperature are likely to be larger and more important than any other factor associated with climate change . Given the predicted increase in temperatures in California in the coming century, this is a key area upon which attention should be focused. Invertebrates require a certain number of degree days to develop from one point in their life cycle to another. The survival, range and abundance of many invertebrate pest species is mediated by temperature. Furthermore, temperature is the dominant abiotic factor that directly affects herbivory . Consequently, the diversity and intensity of insect herbivores increases with rising temperatures and constant latitude . In Multivoltine species , such as the Aphididae and some Lepidoptera, development time is expected to increase with climatic warming, allowing for increased generations within a year . A 2o C temperature rise, which is at the lower end of temperature increases predicted for California in the coming century , may result in 1-5 additional generations/ yr for a range of invertebrates such as insects, mites & nematodes . It is also likely that many pest species will expand their geographical range in a warmer climate, seen already in Britain in several butterfly species . The effect of higher temperatures on overall abundance of herbivorous insects remains unknown in the absence of equivalent data of their natural enemies . While warming speeds up the life cycles of many insects,growing tomatoes hydroponically suggesting that insect pest problems could increase , herbivorous insects may grow more slowly, as they feed on the typically protein poor leaves produced under conditions of elevated atmospheric concentrations of CO2 .

The increase in C:N ratio in plant tissue may cause insects to eat more herbaceous material, thereby causing more damage or change their feeding preferences to satisfy their dietary N requirements, slowing larval development and increasing mortality . Climate change may impacts host species in ways that make them more vulnerable to pests , for example, pine bark beetles would find pine trees easier attack . Adaptation to changing climate would be more rapid for insects than host plants, due to generation time , and the spread of insect pests may be accelerated if host ranges change rapidly due to environmental change or to socioeconomic incentives . . For example, the temporal synchrony of larval emergence of the Winter moth, Operophtera brumata, and bud burst of its host plant, sitka spruce Picea sitchensis are important. A temperature increase of 2o C is not expected to dramatically impact bud burst date; however, larval emergence is likely to advance dangerously ahead of bud burst . However, temperature does not act in isolation to influence pest status. Some insects are unable to cope in extreme drought, while others are disadvantaged by extreme wetness. However, the present forecasts of California’s future precipitation patterns are uncertain, making predictions of this nature difficult. Taken together, these examples highlight the complex climatic and trophic interactions that California agriculture will need to begin to consider in a changing climate .The global pesticide market was valued at $29 billion in 2000, with herbicides, insecticides and fungicides representing 48%, 27% and 19% of expenditure respectively . In addition to the high costs of chemical control, there are growing environmental and health concerns about the use of pesticides and their regulation , and applications must be timed precisely to maximize efficiency and minimize undesired impacts. Under increased temperature scenarios, the number of days that will be suitable for spraying is likely to increase where it is drier and decrease where it is wetter; however, as a result of increased pesticide application, invertebrate pests may build resistance to the chemical or its active ingredient .

Furthermore, the toxicity and/or stability/volatility of the chemical are likely to change under different climatic conditions . An important consequence of chemical spraying is that natural enemies present in the ecosystem are killed, further increasing the need for chemical applications to control pest populations. Health risks to workers and consumers, associated with increased pesticide usage in Californian agriculture, are also of importance. The efficacy of other control methods such as biological control and the use of genetically modified organisms are likely to be impacted by climate change. Factors that impact the abundance and activity of invertebrate pests will similarly impact beneficial invertebrates such as predators, parasitoids, and pollinators. Thus, biological control efforts will need to consider the impacts of climate change on complex pest/natural enemy dynamics. For example, high temperatures tend to decrease the efficacy of the entomopathogenic fungus Beauvaria bassiana in controlling wax moth in soil treated with certain pesticides . In Australia, the effectiveness of Ingard cotton which has been genetically modified to produce a Bt toxin precursor, appears to be greater at a given node when that node is produced at a higher temperature . This adds an additional layer of complexity that needs to be considered as GM crops are grown in some instances to not only reduce pest pressure but to also decrease insect vectored plant pathogens . Taken together, these examples highlight the need for multi-trophic studies of pest, biological control agent and host plant dynamics in a changing climate.Invertebrates not only cause direct damage to crops, but can also act as vectors of disease causing organisms. Environmental conditions play a significant role in vector borne diseases, and the impact of climate change has the potential to shift geographical ranges . Some examples of vectored diseases include Curly Top virus, which affects several hundred varieties of ornamental and commercial crops in California and is vectored by the Sugar Beet leaf hopper, Tomato Spotted Wilt Virus, vectored by Western Flower Thrips and Pierces Disease vectored by the Glassy Winged Sharpshooter. These will be considered in more detail in the following section.

The risk of agricultural yield losses due to disease, weeds and insects, is likely to increase with climate change, but is rarely considered in climate assessments . Disease onset requires a susceptible host,hydroponic growing supplies a virulent or infective pathogen, and a favorable environment. Disease-causing microbes are dependent on temperature and moisture optima for establishment and reproduction, with most diseases occurring in warm and wet conditions . Pathogenicity, or the degree to which the host is harmed by its parasite, depends on this three-part interplay. Disease often occurs outside of the temperature optima of the pathogen and the host, and often results from the host organism being more susceptible than the pathogen to being outside of these optima . Climate change in California, especially in the context on increased temperatures, and its impact upon plant disease development is likely to be of great consequence to California agriculture.An increase in average temperatures of just a few degrees can hypothetically lengthen the growing season as well as the growth rate of a pathogen dramatically . While increased CO2 may increase plant growth, it may also increase pathogen fecundity, thereby negating or reversing positive effects on plant growth, should conditions conducive to disease development, such as increased temperatures, manifest . Similarly, increased O3 and UV-B levels, while harmful to plant tissues, may also harm obligate host pathogens, decreasing plant disease . The global impacts of pathogen outbreaks in agriculture have been profound . One example is the Irish potato famine in the 1840’s, caused by potato late blight . Since the 1960’s millions of livestock and poultry have been destroyed in response to combined outbreaks of Influenza A Virus, Foot and Mouth disease, and Mad Cow Disease alone , with anomalous climate patterns often flagged as alleged triggers to such natural economic disasters . The introduction of new agricultural pathogens through species range shifts will undoubtedly be a major effect of changing climates . Climate-driven pathogen range extensions in terms of both latitude and elevation have been widely reported in mosquito-borne human diseases such as malaria and dengue and yellow fevers ; however, debate exists on whether such range expansions are better attributed to anthropogenic causes . Similar climate-range interactions have been anticipated in aphids by influencing winter survival and spring flight timing .

Evolutionary responses of pathogens are an additional source of uncertainty in changing agricultural systems. It is well known that microbial agents can quickly evolve resistance to antibiotics and herbicides, often within time scales less than a decade . However, adaptation potentials are not unlimited and interactions between pathogen evolution and their environment, having been rarely studied. For example, increased atmospheric CO2 concentrations have been shown to increase fungal disease severity in crop plants in short-term experiments , while in a long-term experiment in the same system, Chakraborty and Datta showed a decreased ability of the fungal pathogen to evolve aggressiveness in elevated CO2 environments, purportedly due to enhanced host resistance. Furthermore, climate change will enable plant pathogens to survive outside their historical geographic range; consequently, climate change may lead to an increases in the significance of pre-existing pathogens as disease agents, or provide the climatic conditions required for introduced pathogens to emerge .In the multi-billion dollar grape industry of California , Pierce’s Disease has caused Riverside County alone $13 million in damage as of 2002, and the state has aided the industry with more than $65 million in control efforts since 1998 Pierce’s Disease is a prominent bacterial disease of California grapes that is caused by Xyllela fastidiosa and vectored by the Glassy-Winged Sharpshooter, a native to the southeastern U.S. that is more mobile than existing leaf hoppers, is limited to climates with mild winters such as southern California . The optimum temperature for growth of the Pierce’s bacterial pathogen is 28°C . Consequently, northern and coastal California grape-growing regions are currently suboptimally cool for Pierce’s Disease. However, under climate change, these regions may face increased risk of establishment of Pierce’s disease. The threat of the glassy winged sharpshooter is not limited to grapevines; its host range includes more than 100 species of plants, including almonds, citrus, peaches, plums, alfalfa and ornamental plants produced by the state’s commercial nursery industry, and therefore has the potential to disrupt the state’s agricultural economy, especially if it will increase under future climate scenarios. In 2004 West Nile virus was reported in horses in more than half of California counties, resulting in a 42% mortality rate of infected animals . Assuming that warming climates lower developmental thresholds for mosquito vectors , WNV incidence could potentially increase in California in areas historically less prone to mosquito outbreaks. Similarly, changes in amounts and timing of precipitation, snow melt and stream flow dynamics , may lead to an increase in the abundance of mosquitoes in California, and hence, WNV. Disease forecasting models are essential in order to be able to quickly respond to high risk trends. In California several crop disease models have been developed and are in use. Downy mildew in lettuce is an example of a disease whose incidence can be predicted by a very simple model; morning leaf wetness after 10 am, influenced by low midday temperature and high relative humidity, directly affect disease incidence . In this system, warming alone may actually reduce disease risk for this pathogen in certain areas; however, with future precipitation patterns uncertain at best, there is need for further information. Interactive risk assessment and forecast models are currently available through the University of California Integrated Pest Management Program for powdery mildew on grapes and tomatoes . The fungal mildews in these systems, as well as others, such as the devastating late blight in potato and tomato , are tightly linked to temperature and precipitation, with severe disease outbreaks occurring in relatively wet winters with mild temperatures such as in El Niño years . Esca, a fungal disease in California table and wine grapes, appears to respond to above-normal rainfall and summer temperatures .