Month: February 2024

Farmers make a wide range of decisions regarding the management of their crops, involving pest management, planting/harvest dates, fertilization, irrigation, and, as we focus on in this study, crop rotation. These decisions are, along with external factors that fall outside farmers’ control, such as weather, likely to affect crop performance and yield substantially. A rigorous quantitative understanding of the factors, including farmer management decisions, that affect crop yield is an essential prerequisite for developing management strategies that maximize yield. There are several possible mechanisms by which the crops previously grown in a field can affect crop yield. First, different crops have different effects on the nutrient composition of the soil, so the identities of crops previously grown in a field can affect nutrient availability and crop yield. For example, nitrogen-limited crops can benefit from rotation with nitrogen-fixing legumes, and phosphorus nutrition in California cotton is shaped by whether or not the previous crop received phosphorus fertilizer. Second, certain crops may increase the local abundance of particular insect pests and pathogens. Since different crops are often susceptible and resistant to different pathogens and pests, the identities of the crops recently grown in a field can affect yield. For example, if one crop increases local abundances of an insect pest that also attacks a second crop, planting the second crop immediately following the first may lead to decreased yield resulting from attack from the built up local pest population. In contrast,raspberry container size such a yield depression could potentially be averted if the second crop were planted following a crop that does not lead to local accumulation of the pest.

In monocultures of wheat, substantial yield declines have been noted and attributed to the buildup of the soil-borne fungal pathogen Gaeumannomyces graminis. Third, many studies have shown that a field’s crop rotational history can strongly affect weed densities. Numerous other mechanistic explanations for the yield effects of crop rotation have also been suggested. Crop rotation has been practiced for thousands of years; evidence for its inception dates back to ancient Roman and Greeksocieties. Experimental studies on the effects of crop rotation first appeared in the early 20th century, revealing that growing crops in rotation led to increased crop yields of up to 100% compared to continuous planting of a single crop. Interest in the yield effects of crop rotation waned during the middle of the 20th century, due to the increasing availability of cheap fertilizers, insecticides, and herbicides. However, crop rotation continues to be a relevant and important practice; low-input farming remains desirable due to the costs of fertilizers and pesticides, and fertilizer and pesticide applications can often not fully compensate for the benefits afforded by crop rotation. In addition, the significant environmental and public health concerns surrounding fertilizer and pesticide use highlight the desirability of methods of increasing crop yield through alternative methods such as crop rotation. The effects of rotational histories on yield are well understood for some crops, such as corn, where rotation is recognized to be crticial in avoiding the buildup of corn root worms. However, for many crops, the direction, magnitude, and mechanism of the effect of crop rotational histories on crop yield remain poorly understood.

Cotton is one such crop. Experimental field studies of the effect of crop rotation on cotton yield have demonstrated increased cotton yield, compared to continuous cultivation of cotton, when cotton is grown in rotation with sorghum, corn, and wheat. Despite these useful results, only a small subset of possible rotations has been studied, experiments have been restricted to plots significantly smaller than typical commercial cotton fields, and mechanisms for these effects remain poorly understood. To help address these limitations, we seek to expand upon this work by exploring the effects of crop rotational histories on yield in commercial cotton fields in California, using an ‘‘ecoinformatics’’ approach capitalizing on existing observational data gathered by growers and professional agricultural pest consultants. In recent years, there has been a surge in research and interest involving the rapidly emerging field of ‘‘big data.’’ The big data movement has been fueled by several developments, including a dramatic increase in the magnitude of data generation, an improved ability to cheaply store, manipulate, and explore massive datasets, and the development of new analytic methods. Most importantly, the movement has been driven by a growing realization that existing data, and data generated as a byproduct of our everyday lives, can be leveraged to explore key questions about nature and human behavior, even if the data were not collected for this purpose. Ecoinformatics is a nascent field focused on harnessing the power of big data to address questions in environmental biology. Ecoinformatics approaches typically involve the analysis of large datasets, the synthesis of diverse data sources, and the analysis of pre-existing, observational datasets. In some commercial agricultural settings, farmers, along with hired consultants, collect a great deal of regular data about their fields that are used to guide real-time crop management decisions, such as the timing of pesticide applications.

By capitalizing on data that are already generated as a byproduct of commercial agriculture, ecoinformatics provides a low-cost means of obtaining a large dataset that can be used to explore key questions in agricultural biology, some of which might be too difficult or too costly to explore experimentally. Furthermore, the large size of datasets created for ecoinformatics can afford greater statistical power than could possibly be generated through experimental work. Experimentally studying the yield effects of crop rotational histories is challenging for several reasons. There are a plethora of possible rotational histories, which means that a large number of treatments would be required to explore the space of possible rotational histories thoroughly. Furthermore, experimentally studying effects of crop rotations requires experiments spanning several growing seasons, which may be logistically challenging. Finally, in order to maintain realism and applicability to commercial fields, which are typically quite large, sizeable experimental plots would be required, especially in light of research suggesting that landscape composition as far as 20 km from a focal field can affect the densities of agricultural pests in that field. While yield effects of non-mobile factors such as soil characteristics may be readily detected through small plot experimentation, the effects of highly mobile arthropods may only be detected at much larger spatial scales. An ecoinformatics approach offers attractive solutions to these challenges. Since we analyze a large preexisting dataset that includes over a thousand records,raspberry plant container a diversity of the possible crop rotational histories already exists in the dataset. In addition, our dataset spans 11 years of data, so the data span the temporal scale necessary to ask questions regarding effects of multi-year rotational histories. And, since the data come from the exact setting where we wish to apply our results, the data are realistic and capture the appropriate spatial scale of commercial agriculture. First, we sought to identify which crop rotational histories are associated with increased and decreased cotton yield, and to quantify these yield effects. We then explored possible explanations for the yield effects identified in the previous step by examining the associations between crop rotational histories and pest abundance.We employed a hierarchical Bayesian modeling approach, fitting linear mixed models to explore our questions about the effects of crop rotational histories on cotton yield. Mixed models combine the use of random effects and fixed effects, making them ideally suited for analysis of data that are structured, or clustered, in some known way, such that separate observations from within clusters are expected to be similar to one another. When we model a source of clustering using a random effect, we assume that each cluster-specific parameter was drawn from a common distribution, and we estimate the parameters of this distribution from the data. We use this common distribution as the prior when calculating the posterior distribution of each cluster-specific parameter. The parameters of the distribution of cluster-specific parameters have posteriors that are estimated from the data, typically after assuming uninformative priors for the hyperparameters. Using a common, empirical prior for all cluster-specific parameters allows pooling of information across clusters, so that data from all clusters can help inform estimates of every other per-cluster parameter. Assuming all clusters are the same introduces high bias and tends to underfit the data, whereas estimating fixed effects for each cluster introduces high variance and tends to overfit the data; however, using a random effect provides an optimal compromise between introducing bias and introducing variance.

In this dataset, there are several plausible sources of clustering. 1. First, we expect the data to be clustered by field, since there likely exist field-specific factors that affect yield, such as soil characteristics, local climate, and grower agronomic and pest management practices. We controlled for variable yield potential between fields by including field identity as a random effect in our models. Random effects allow pooling of information across clusters, so they are particularly useful when there are few observations from some clusters – a situation in which it is difficult to accurately estimate each percluster parameter with only the data from that one cluster. Since there are three or fewer records for 78% of the fields in our database, we feel that including field as a random effect was preferable to trying to estimate field-specific fixed effects with very few observations per field. Additionally, including field as a random effect provides a straightforward way to make predictions for fields not represented in our database. Since modeling field as a random effect involves sample a field-specific parameter from this distribution if we wish to make predictions about a previously unobserved field. Uncertainty in this field-specific parameter can be propagated by simulating many samples from this distribution, while simultaneously accounting for uncertainty in the parameters of this distribution. However, if we were to model field as a fixed effect, we would not estimate a distribution of field-specific parameters. We would only estimate parameters for the specific fields in our database, leaving us with no obvious way to make inferences about new fields. 2. Second, we expect that our data are clustered by year, since there is substantial between-year variability in climate, particularly in the winter and early spring. Climatic variables can affect crop performance, planting date, and insect pest populations, all of which can in turn affect cotton yield. To control for and quantify variation in yield due to year-specific factors, we included year as a random effect in our models. Our reasons for including year as a random effect are the same as those for field: there are few observations from some years, and we may wish to make predictions for future years not covered by the existing database. All models were fit using a No-U-Turn Sampler variant of Hamiltonian Markov Chain Monte Carlo implemented in Stan version 1.3.0, accessed through the rstan packing in R. We ran three chains from random initializations, each with 10,000 samples, and discarded the first 5,000 samples from each as burn-in. Inferences were based upon the remaining 15,000 samples. We checked convergence by making sure that ^ R, an estimate of the potential scale reduction of the posterior if sampling were to be infinitely continued, was near 1.To explore the yield effects of the crop grown in the same field the previous year, we fit a linear mixed model with yield as the response variable. The predictor variable of primary interest was the identity of the crop grown in that field the previous year, which was included as a fixed effect. Given that we are working with an observational dataset, a critical step in order to make meaningful inferences about the variable of primary interest – the crop grown the year before – was to control, to the extent possible, for potentially confounding variables that could generate spurious correlations and taint the validity of our inferences about crop rotation. To control for variable yield potential between fields and years, field and year were included in the model as random effects. The field terms control for the possibility that some fields may have higher yield potential due to their location, soil characteristics, or growing practices; the year terms control for the substantial year-to-year variation in cotton yield, which likely results from yearly weather differences. A term indicating cotton species was included in the model to account for yield differences between cotton species.

When cohort participants were 6 and 12 months old, most households showed signs of moderate or extensive mold at either visit. At age 7, based on maternal report, the majority of families was living below the Federal Poverty Level, 15.7% of cohort children experienced a runny nose without a cold within the past year, 16.3% displayed asthma symptoms, and 6.1% were currently taking asthma medication. Table 2 shows the distributions of wind-weighted fumigant use within 8 km of CHAMACOS residences during the prenatal and postnatal exposure periods. Methyl bromide and chloropicrin were the most heavily used fumigants during the prenatal period, with mean ± SD wind-adjusted use of 13,380 ± 10,437 and 8,665 ± 6,816 kg, respectively. Reflecting declines in methyl bromide use, the use of chloropicrin was greater than the use of methyl bromide during the postnatal period, with median values of 127,977 and 109,616 kg during the 7 years, respectively. When we examined correlations within each fumigant, use within 3, 5, and 8 km from the home was highly correlated for each fumigant . Fumigant use during the prenatal and postnatal periods was also highly correlated for methyl bromide and chloropicrin, but was not correlated for metam sodium use and was inversely correlated for 1,3-DCP use . We also examined correlations among fumigants and observed high correlations between prenatal methyl bromide and chloropicrin use and between prenatal metam sodium and 1,3-DCP use . There were negative correlations between prenatal methyl bromide and chloropicrin use with prenatal metam sodium and 1,3-DCP use .Adjusted associations between a 10-fold increase in the amount of fumigants applied within 8 km of the home and the highest lung function measurements are presented in Table 4. We did not observe any significant adverse relationships between prenatal or postnatal fumigant use within 8 km and lung function. A 10-fold increase in wind-adjusted prenatal methyl bromide use within 8 km was associated with higher FEV1 and FEF25–75 . Additionally, a 10-fold increase in wind-adjusted prenatal chloropicrin use within 8 km was positively associated with FEF25–75 .

Associations between methyl bromide and chloropicrin use and lung function observed in the prenatal exposure period were not observed in the postnatal period. Results were similar, although no longer statistically significant,plastic gardening pots for prenatal methyl bromide and chloropicrin use within 5 km of residences . There were no associations between fumigant use within 3 km of residences and lung function . We did not observe associations between postnatal fumigant use at any distance and lung function measurements or between fumigant use during the year prior to the assessment and lung function measurements . In sensitivity analyses using multivariable models including other pesticide exposures that have been previously related to respiratory symptoms and lung function including childhood urinary DAP metabolites , proximity to agricultural sulfur use during the year prior to lung function assessment and prenatal DDT/DDE blood concentrations , the results were very similar to those presented in Tables 3 and 4. For example, the relationships between prenatal methyl bromide use within 8 km were very similar for FEV1 and FEF25–75 . Prenatal fumigant use was generally not correlated with other pesticide exposures that we found to be associated with lung function in this cohort, except for weak correlations between agricultural sulfur use within 1 km during the year prior to spirometry and prenatal use of metam sodium and 1,3 – DCP with r = 0.14 and r=0.26 respectively. The results were very similar when we only included children with two acceptable reproducible maneuvers in the analyses . The results were also similar when we excluded those currently using asthma medication, excluded the one outlier for FEV1 models or used inverse probability weighting to adjust for participation bias . Risk ratios estimated for asthma symptoms and medication using Poisson regression were nearly identical to the ORs presented in Table 3 and Supplemental Table 2. We did not observe effect modification by asthma medication use. Maternal report of child allergies modified the relationship between FEV1 and prenatal proximity to methyl bromide use and we only observed higher FEV1 among children without allergies .

After adjusting for multiple comparisons, none of the associations reached significance at the critical p-value 0.002 based on the Benjamini-Hochberg false discovery rate. This is the first study to examine lung function or respiratory symptoms in relation to residential proximity to agricultural fumigant use. We found no significant evidence of reductions in lung function or increased odds of respiratory symptoms or use of asthma medication in 7-year-old children with increased use of agricultural fumigants within 3 – 8 km of their prenatal or postnatal residences. We unexpectedly observed a slight improvement in lung function at 7 years of age with residential proximity to higher methyl bromide and chloropicrin use during the prenatal period and this improvement was limited to children without allergies. Although these results remained after adjustment for other pesticide exposure measures previously related to respiratory symptoms and lung function in our cohort, they do not remain significant after adjustment for multiple comparisons. There is a strong spatial pattern of methyl bromide and chloropicrin use during the pregnancy period for our study because of heavy use on strawberry fields near the coast at the northern portion of the Salinas Valley . There could be other unmeasured environmental or other factors that are confounding the relationship we observed between higher prenatal fumigant use and improved lung function. Previously published studies of prenatal exposure to air pollutants and lung function have generally observed links to alterations in lung development and function and to other negative respiratory conditions in childhood, and plausible mechanisms include changes in maternal physiology and DNA alterations in the fetus . Improved lung function was associated with higher estimates of recent ambient exposure to hydrogen sulfide in a study of adults living in a geothermal area of New Zealand . However, hydrogen sulfide has been shown to be an endogenously produced “gasotransmitter”,blueberry pot size with anti-inflammatory and cytoprotective functions , and is being explored for its use for protection against ventilator-induced lung injury .

In previous studies of this cohort, we found increased odds of respiratory symptoms and lower FEV1,  and FVC  per 10-fold increase of childhood average urinary concentrations of metabolites of organophosphate pesticides . Other studies of prenatal pesticide exposure and respiratory health in children have mostly evaluated exposure using cord blood concentrations of DDE, a breakdown product of DDT, and have observed an increased risk of respiratory symptoms and asthma with higher levels of DDE . Most studies of postnatal pesticide exposure and respiratory health in children have utilized self-reported information from mothers to assess pesticide exposure and have observed higher odds of respiratory disease and asthma with reported pesticide exposure . None of the previous studies of pesticide exposure and respiratory health have specifically evaluated fumigants. Another strength of the study is that CHAMACOS is a prospective cohort followed since pregnancy with extensive data on potential confounders of respiratory health and other measures of pesticide exposure. Our study also had some limitations. We did not have information on maternal occupational exposure to fumigants or the geographic location of maternal workplaces during pregnancy, and we did not have the location of schools during childhood. These limitations likely resulted in some exposure misclassification during both the prenatal and postnatal periods. An important consideration in this study is that we estimated fumigant exposure using proximity to agricultural fumigant applications reported in the PUR data, which is not a direct measure of exposure. However, the PUR data explains a large amount of the variability of measured fumigant concentrations in outdoor air . In conclusion, we did not observe adverse associations between residential proximity to agricultural fumigant use during pregnancy or childhood and respiratory health in the children through 7 years of age. Although we did not observe adverse effects of fumigants on lung function or respiratory symptoms in this analysis, we have seen adverse associations in previous analyses of the CHAMACOS cohort between residential proximity to higher fumigant use and child development. We observed an association between higher methyl bromide use during the second trimester of pregnancy and lower birthweight and restricted fetal growth . We also observed decreases of ~2.5 points in Full-Scale intelligence quotient at 7 years of age for each 10-fold increase in methyl bromide or chloropicrin use within 8 km of the child’s residences from birth to 7 years of age . Future studies are needed in larger and more diverse populations with a greater range of agricultural fumigant use to further explore the relationship with respiratory function and health.

The fact that the annual water used in growing California agricultural products is far greater than the total urban water use is well known . As pressures on water resources intensify globally, there is a growing interest in evaluating the complex ways in which human activities impact the world’s water resources . Globally, the majority of water consumption is used in the production of agricultural products . As a result, the agriculture industry is by far, the most dominant water-using sector. To assess the amount of water used throughout the production and distribution process to produce a final product, researchers have used the term ‘water footprint’, to describe this quantity . Water footprint assessment had emerged as a tool for quantifying consumption of goods and services in one location and the cumulated water use associated with the production of those goods and services in other distant locations . Following the introduction of the water footprint concept, various studies were conducted to quantify global virtual water footprints and assessed virtual water flows between nations , , and . Virtual water flows and water footprint assessments became important elements in evaluating local, national, and global water budgets as reported by Chen and Chen , Duarte et al., , Guan and Hubacek , Hubacek et al. , Velazquez , Yang et al. , Yu et al. , Zhao et al., . Mekonnen and Hoekstra showed that the international virtual water trade in agricultural and industrial products were 2320 billion cubic meter per year in the period 1996-2005, equivalent to 26% of the global water footprint of 9087 Gm3 . noted that although practically, every country participates in the global virtual water trade, few governments explicitly consider assessing virtual water footprint and its impact in their management policies. The majority of the water footprint studies have examined international virtual water footprints between nations . Few have also analyzed the virtual water footprints at a sub-national or state level such as regions within Australia , China , India , and Spain . Within the United States, two studies have been conducted. Fulton et al., reported that California imported more than twice virtual water as it exported and that more than 90% of its water footprint is associated with agricultural products. Mubako et al., quantified virtual water for California and Illinois, and reported that the two states were net virtual exporters in agricultural water trades. Previous studies on virtual water footprints only aimed to quantify the cumulative water footprint required to produce a final product. No study has focused specifically on quantifying the physical water content contained in agricultural commodities and the associated evapotranspiration being exported. The total exported water in agricultural products is distinctively different than the virtual water footprint in that the former is physically exported outside of a geographical boundary, whereas the majority of the water used in quantifying virtual water footprint may remain within the local geographical boundary and be absorbed or reused in some ways. The exported water content in crops is permanently lost and is no longer available for natural hydrologic cycle. This research seeks to fill the gap of knowledge by quantifying the exported water contained in agricultural products and associated induced evapotranspiration. The research also seeks to analyze the energy advantage of applying reclaimed water in crop irrigation, by assessing the carbon footprint reduction and monetary savings for using reclaimed water in arid and semi-arid regions. Fresh water availability has always been the major constraint to growth and development in California.

The Salinas River sampling site has been used as a least-impacted reference site in previous toxicity studies and is generally classified as non-toxic, based on acute exposure studies. This increase in potency after a rain event is consistent with an influx of pesticides, and the chemical analyses show higher levels of several pesticides of concern in November at all three sites . Climate change is altering rainfall patterns in many areas of the world and understanding how these changes may impact sensitive aquatic systems is crucial for monitoring water quality. Surface water exposure caused significant changes in D. magna swimming behavior both before and after a first flush event, even at low concentrations. In September, prior tofirst flush, we detected strong dose-response patterns in total distance moved and log-linear dose response in photomotor response. Daphnia magna exposed to all concentrations of surface water in September increased their movement in response to light stimulus, while control groups reduced their activity. This may have implications for survival in natural populations. Individuals who cannot respond to predator cues, or that show impaired and/or altered responses, may have an increased risk of predation. It is important to note that changes in swimming behavior in organisms exposed to water samples from Alisal Creek in September may have been partially capturing a lethal response. This treatment group had significant mortality in all exposure concentrations , so it is possible these individuals were exhibiting not only sublethal,planting blueberries in pots but also delayed lethal toxic responses. Future studies should consider including recovery periods in their experimental design and analyses to parse out whether behavioral impacts are reversable, indicative of long-term effects, or even subsequent mortality.

Due to the high mortality observed for Quail Creek in September, we were unable to make any behavioral comparisons. It is notable that the level of methomyl detected at this site was greater than three times the EPA chronic fish exposure level, and it is likely that methomyl represents a main driver of the toxicity for this site. It is possible that additional contaminants are present at this site, which were not included in our analysis. Many pharmaceuticals are known to cause hyperactivity and have been detected in wastewater at other sites in California. Taken together, these findings illustrate the importance of conducting sublethal assessments to link physiological responses to chemical monitoring data. After the first flush , we measured hypoactivity for all sites during at least one light condition, in at least one concentration. Many of the pesticides we detected in surface water samples are known to reduce the swimming speed and distance of D. magna at concentrations relevant to those detected in our samples. We detected changes to the photomotor responses of D. magna exposed to low concentrations of surface water from all three sites when compared with controls, demonstrating biologically relevant impacts. Despite low mortality observed in the Salinas River site during both testing dates, we detected altered behavior even at the highest dilution of 6% ambient water in November. Hypoactivity and altered photomotor responses may reduce the capacity of D. magna to follow normal behaviors, such as patterns of diel vertical migration and horizontal distribution, thus increasing predation risk and reducing overall fitness. We measured significant changes in the swimming behavior of D. magna after acute exposures to CHL and IMI in single and binary chemical exposures, and as components of agricultural surface waters both before and after a first flush event.

Surface waters contained complex mixtures including CHL and IMI, but also other pesticides of concern including neonicotinoids, pyrethroids, carbamates, and organophosphates. We determined that swimming behaviors of D. magna are sensitive endpoints for the sublethal assessments of the tested pesticides, and for surface water exposures. We detected chemical-specific changes in D. magna swimming behavior for both CHL and IMI exposures. Imidacloprid exposure at environmentally relevant concentrations caused hypoactivity for both concentrations tested, across both dark and light conditions, following a dose-response pattern. The increase in activity over the light period represents a return to baseline following a change in light conditions. Our results are consistent with previous findings: IMI negatively impacts nerve conduction and alters swimming behavior in D. magna and is known to inhibit acetylcholinesterase . Past research has shown AChE inhibition is linked to changes in swimming response, and that a 50% decrease in AChE activity can cause enough change in swimming behavior in D. magna to be described as toxic . In a recent study examining the effects of IMI on the amphipod Gammarus fossarum, IMI stimulated locomotor activity at low exposure concentrations and inhibited activity at higher concentrations . Daphnia magna are particularly tolerant to neonicotinoids, illustrating the potential for impacts in other more sensitive organisms known to inhabit IMI-polluted waterways. We detected significant hypoactivity in individuals exposed to CHL under dark conditions. This is consistent with previous studies on D. magna demonstrating that CHL is a known neurotoxicant for this species, causing changes in muscle contraction via interaction with the ryanodine receptor. Low levels of CHL exposure have been shown to produce dose-dependent inhibition of swimming, and decreased responses to light stimulation in a recent study. Another recent study examining effects of single chemical exposure to CHL and IMI, among other chemicals, at low concentrations had effects on total distance moved of D. magna.

We observed hypoactivity under dark conditions and hyperactivity under light conditions for D. magna after exposure to binary mixtures of CHL and IMI. Hyperactivity could suggest a possible disruption of signal transmission in the vision or nervous systems and has been observed for IMI exposures at low exposure levels in other studies. The hyperactivity observed in the low IMI exposure group was notable in that the response was inverse from both single chemical exposures performed at the same concentrations, potentially indicative of an antagonistic response. Our finding is partially consistent with Hussain et al.; however, where investigators also found hyperactivity under light conditions but no significance under dark conditions. It is relevant to note that our experimental design differed from that of Hussain et al. , who used one exposure vessel containing 50 Daphnids per treatment group, whereas we used fewer Daphnia per exposure vessel, with six exposure vessels per treatment. For future studies, increased replication could improve the ability to determine whether small changes in total distance moved could also be significant. Considering the significance of our other treatments and endpoints, and that our replication exceeded many previously published studies, we propose that our experimental design was sufficient to detect many sublethal effects . Sublethal impacts can result in ecologically relevant effects on individual fitness, populations, and communities. In pesticide-contaminated aquatic environments, overall invertebrate biomass and diversity are reduced as sensitive individuals and species decline . With the increasing number of pesticides being detected in waterways worldwide,blackberries in containers rapid and standardized testing approaches are urgently needed. For many species and chemicals of interest, biochemical reactions can visually manifest via behavioral changes, making behavior a highly integrative and informative endpoint for exposure. Meta-analysis of behavior in comparison to other toxicological endpoints such as development, lethality, and reproduction, showed that behavioral analyses are advantageous to assess the effects of environmental chemicals due to their relative speed and sensitivity. Behavioral assays possess great potential as rapid, high throughput monitoring tools. The world now seems prepared to seriously consider agricultural trade liberalization and domestic food and farm policy reform. The economic summits of the major western countries, the Organization for Economic Cooperation and Development , the World Bank, the International MOnetary Fund, the General Agreement on Tariffs and Trade , and numerous other international agencies now recognize the necessity of multilateral and phased liberalization. In other words, a dramatic reduction in protection for agriculture throughout the world would appear to be the right answer. Simple economic analysis has demonstrated that, in a world in which pure competition maximizes net economic payoff, the deadweight losses resulting from current policy interventions in food and agriculture are enormous.

Unfortunately, we do not live in such a world: Only second-best outcomes are possible, governments do not maximize social welfare, pure non–distortionary–that is, decoupled– transfers do not exist, political and economic markets are not separable, and policies for other sectors–especially general macroeconomic policies–are not perfectly designed and implemented. Simply put, there are many complications in evaluating agricultural and food policy reform. This paper will examine one in particular–the macroeconomic risk nations face in the implementation of food and agricultural policy reform. In all of the recent studies of agricultural trade liberalization and agricultural policy reform, little if any attention has been paid to the macroeconomic environment that might exist during the implementation phase of various proposals. This is indeed surprising because the origins of many farm policies can be traced directly to the macroeconomic environment. Moreover~ the dynamic adjustment paths that would evolve following the implementation of particular reform proposals would be heavily dependent upon macroeconomic conditions~ such as the level of real interest rates and exchange rates~ the nature of monetary and fiscal policies–whether expansionary or deflationary– and so on. This paper focuses on four major themes. First~ macroeconomic and international linkages are significant and must be recognized in any framework for policy design and reform. Second~ the intercountry linkages of both agricultural and macroeconomic policies are especially important for less-developed countries . Third~ political economic markets for policy reform exist and governments throughout the world have an opportunity to supply reform through the reduction of transaction costs. Transaction costs can be reduced through alternative compensation schemes which are motivated by behavioral analysis of political economic markets. And fourth, macroeconomic and international linkages are a major component in the design of flexible agricultural policies that can respond to changing conditions. These themes·are used to examine agricultural policy reform and trade liberalization in the current environment.Throughout much of the developed world~ macro policies in the two decades following World War II afforded a unique period of macroeconomic stability. As a result~ concern regarding the macroeconomic linkages with food and agricultural systems largely disappeared. In the early 1970s with the major changes in monetary polices and central bank behavior, macroeconomic linkages were once again recognized as prime factors complicating agriculture and food policy. The roller coaster ride that agriculture has experienced over the last two decades has been significantly influenced by macro and international linkages . Recent history stands in sharp contrast to the basic stability of the 1950s and 1960s. This roller coaster is not unprecedented. For example, the period 1900 through 1915 is surprisingly similar to the 1970s, and the late 1920s through 1930s have some of the same characteristics of the 1980s. A longer historical perspective demonstrates that macroeconomic disturbances and their links to agricultural sectors throughout the world were central to the emergence of direct governmental intervention in food and agricultural systems. For example, in the case of OECO countries, there have been abrupt increases in governmental intervention during periods of macroeconomic contractions accompanying downward movements in agricultural prices. The first major wave of increasing intervention.in agriculture occurred during the last quarter of the 19th Century, following several decades of trade liberalization. Prior to this, agricultural trade had expanded dramatically due to the removal of tariffs and import quotas and to the increasing availability of low price grain from the United States and Europe. The protectionism following this trade expansionary period was motivated by what was then referred to as Europe’s great depression.Policy responses varied across countries. England alone maintained a staunch free trade position while Germany, France, and Italy restored agricultural tariffs from the mid-1880s onward. In Denmark and the Netherlands, falling grain prices encouraged the expansion of livestock activities. In the the United States, despite expanding grain exports, farmers did not ignore depressed prices. The period from 1873 to 1896 witnessed increasing levels of farmer mobilization through the Grange and populace movements. Farmer demands were wide ranging, but a major objective was a change in banking policy to promote inflationary expansion of money supplies. Lobbying efforts to this end continued into the Twentieth Century and were partially responsible for the institutional changes that created the Federal Reserve in 1913 and the federal land banks in 1916. The U. S. government’s massive intervention in agriculture in the 1930s followed a farm crisis that had its origins in the macroeconomic adjustments after World War I.

California has committed to cutting greenhouse gas emissions by 40% of 1990 levels by 2030. As a sector, agriculture is responsible for 8% of state emissions. Approximately two-thirds of that is from livestock production ; 20% from fertilizer use and soil management associated with crop production; and 13% from fuel use associated with agricultural activities . California plays an essential role in the nutritional quality of our national food system, accounting for, by value, roughly two-thirds of U.S. fruit and nut production, half of U.S. vegetable production and 20% of U.S. dairy production. Assembly Bill 32, California’s primary climate policy law, adopted in 2006, has spurred research into practices and technologies that could assist in reducing emissions and sequestering carbon. Here we report on more than 50 California-based studies prompted by this landmark legislation. We note that the California Department of Food and Agriculture, California Air Resources Board, California Energy Commission and California Department of Water Resources have been critical to funding much of the science reviewed here. This article grew out of conversations with state agencies concerning the need for a review of the current evidence base to inform emissions-reduction modeling and revisions to the state Climate Change Scoping Plan , which specifies net emissions reduction targets for each major sector of the California economy . It is important to note that the Scoping Plan states that work will continue through 2017 to estimate the range of potential sequestration benefits from natural and working lands . With over 76,000 farm and ranch operations in California, covering about 30 million acres , there are no one size fits all solutions. But as we outline below,raspberry container there are numerous opportunities to both reduce GHG emissions and sequester carbon across diverse agricultural operations — small to large, organic and conventional, crop and livestock.

Perhaps most importantly, many of these practices have cobenefits for water conservation, restoration and conservation of natural lands, or farm economics. Since 1984, farming and grazing lands have been converted to urban development at an average rate of 40,000 acres per year . At this rate, and considering the higher rate of emissions from urban versus agricultural land, slowing agricultural land conversion represents one of the largest opportunities for agriculture to contribute to California’s climate plan. Research from one county estimates that GHG emissions associated with urban landscapes are up to 70 times greater per acre than those from irrigated farmland when human emissions related to transportation, electricity, natural gas, and water are accounted for . With continued population growth in the state, policies that promote more energy efficient patterns of urban development are critical to meeting climate targets and preserving irreplaceable farmland. Models show that coupling such urban development policies with farmland conservation could reduce transportation and building related emissions from new residential development by 50% by 2050 under a low-emissions scenario . With 80% of California’s most productive rangeland privately owned, losses are projected at 750,000 acres by 2040 . Conversion of rangeland to urban uses may increase GHG emissions up to 100-fold depending on how the rangeland is managed, and conversion to irrigated agriculture may lead to increases of up to 2.5-fold . Land-use-related policies to reduce GHG emissions in California are still at an early stage. Several new incentive programs warrant future research to optimize their impact. These include the Sustainable Agricultural Lands Conservation Program , for purchase of conservation easements on farmland at risk of suburban sprawl development; the Affordable Housing and Sustainable Communities Program , supporting development of affordable housing within existing urban areas; and the Transformative Climate Communities Program , slated to provide GHG-reducing planning grants to disadvantaged communities beginning in 2017. Together with legislation requiring a regional Sustainable Community Strategy, these can create a land use planning framework in California to preserve farmland, reduce GHG emissions, and achieve other co-benefits such as improved quality of life, public health and social equity.

Soils are complex biological systems that provide ecosystem services and can be managed to store carbon, reduce emissions and provide environmental and economic co-benefits. The diversity of California agriculture requires different management strategies to mitigate GHG emissions or sequester carbon. Soil GHG emissions increase with soil moisture and nutrient availability. Significant reductions in GHG emissions can be achieved by shifting management practices to more efficient irrigation and fertigation systems such as micro-irrigation and subsurface drip. A comparison of subsurface drip versus furrow irrigation showed decreased GHG emissions in the former . While cover crops often increase GHG emissions, integrating more efficient irrigation with cover crop practices decreased nitrous oxide emissions two- to three-fold in California processing tomatoes . In semi-arid regions such as California, the long term implementation of no-till practices reduced emissions by 14% to 34%, but only after 10 years of continuous management. Under shorter time horizons, emissions increased by up to 38% . Socioeconomic and biophysical limitations unique to California have led to low no-till adoption rates in California of roughly 2% . Improved nitrogen management provides a high potential for reductions in emissions, including emissions associated with applied fertilizer as well as emissions related to the production and transport of inorganic nitrogen fertilizer . N2O emissions respond linearly to fertilizer application in lettuce, tomato, wine grape and wheat systems in California . However, once fertilizer rate exceeds crop demand, emissions increase at a logarithmic rate . Fertilizer source has been broadly shown to influence N2O emissions . Only a few California studies compare synthetic fertilizer sources. One shows that ammonium sulfate reduced N2O emissions approximately 0.24 to 2.2 kg N per acre compared to aqua ammonium . Another study of comparing fertilizer sources found emissions reductions of up to 34% ; however, the results were not statistically significant. Recently, California research has shown that the use of manure and green waste fertilizers can increase emissions when applied to the soil surface , particularly if their use is not timed to crop demand . Fertilizer source and timing, along with the use of nitrification inhibitors, are key areas for future research in the California context. Management practices have the potential to increase total soil carbon, but the magnitude and persistence of sequestration is dependent on inputs and time. In grasslands, pilot studies of carbon sequestration associated with compost application are being conducted to validate early findings throughout the state . For cultivated systems, in two long-term projects at UC Davis, soil carbon increased 1.4 and 2.3 tons per acre in the top 12 inches of soil over 10 years in cover cropped and organically managed soil, respectively . In an ongoing experiment at the UC Agriculture and Natural Resources West Side Research and Extension Center, no-till combined with cover cropping and standard agronomic practice in a tomato-cotton rotation system has increased soil carbon 5.3 tons per acre over 15 years compared to the standard tillage, no cover crop treatment .

In these two long-term studies,growing raspberries in container the soil carbon increase occurred between 5 and 10 years. However, when cover cropping and compost inputs were ceased at the first site , it led to a rapid loss of soil carbon. This shows that soil carbon sequestration is highly dependent on annual carbon inputs and if management changes, soil carbon is prone to return to the atmosphere. Given the reality of inconsistent management, rates of soil carbon sequestration that can be expected in row crop systems practice are perhaps 10% of the values seen in these long-term research trials, namely in the range of 0.014 to 0.03 tons per acre per year . If soil carbon sequestration and storage are priorities, management plans and incentive structures should account for the wide variability of California soils and the need for consistent management over time. While any single soil and nutrient management practice may have limited impact on GHG emissions, many have well-documented co-benefits, including reductions in erosion, improved air quality , reduced farm machinery fossil fuel use , reduced nitrogen leaching , enhanced water infiltration and reduced soil water evaporation , and increased carbon stocks below the root zone to improve carbon sequestration .Integrated or diversified farming systems are multipurpose operations that may produce several commodities and utilize renewable resources. Examples include integrated crop and livestock systems; organic production; orchard and annual crop intercropping; use of perennial, salt-tolerant grasses irrigated with saline drainage water on otherwise marginal land; and pastures improved by seeding beneficial plants such as legumes. Through reliance on biological processes to build healthy soils and support above and below ground biodiversity, diversified systems offer potential GHG emission reductions . Also, resilience to climate perturbations can occur by spreading economic risks across multiple farm products and by relying on on-farm resources and biodiversity, with less dependence on synthetic fertilizer and pesticides to improve soil and crop health . Other environmental co-benefits can include more efficient use of water, improved water and soil quality, pest reduction or suppression, or enhancement of wildlife habitat and biodiversity. These systems have been shown to reduce soil nitrate and nitrous oxide emissions, and increase carbon sequestration both in soils and above ground biomass . For example, frequent addition of various types of organic inputs increases labile and resistant soil carbon over a period of several years, so that soils exhibit more tightly coupled plant soil nitrogen cycling. In turn, plant nitrogen demand is adequately met, but losses of nitrate are minimized . In another case, an organic vegetable production system, the annual use of cover crops over 6 years led to greater increases in microbial biomass carbon pools, and compost additions increased measured soil organic carbon pool and microbial diversity in comparison to a cover crop grown every fourth year . Many of these studies examined California organic farms where multiple practices are often stacked, such as combining organic soil amendments, integrating cover crops into crop rotation for year-round plant cover and reducing tillage. In addition, farm scaping with perennials on field margins and maintenance of vegetated riparian corridors sequester carbon in the soil and woody biomass of trees and shrubs . Planting native woody species tolerant of drought for hedgerows, or resistant to water flux in riparian corridors, is a way to ensure adaptation and growth over many decades. Diversified, multipurpose systems provide other co-benefits depending on the set of practices involved. Practices that increase soil carbon also improve soil structure, nitrogen-supplying power and water-holding capacity . For example, a practice like cover cropping also can suppress weeds, influence crop nutrition and quality, especially in perennial systems like wine grapes, and provide habitat for beneficial predators . Filter strips and riparian corridors can reduce soil erosion and thereby diminish contamination of surface water with valuable soil and nutrient resources, and pathogenic microbes . Hedgerows have been shown to increase pollinators and other beneficial insects in California . Given the promise for multiple co-benefits, more types of California diversified systems deserve study, which would provide a better basis for metrics to evaluate their long-term contributions to climate and other goals. Intensive livestock operations, particularly the state’s large dairy sector, produce two-thirds of California’s agricultural GHG emissions, and thus are a primary target for state climate regulations as well as incentives for emission reduction. At the same time, policies should account for the already high levels of resource efficiency in the California dairy sector. A key climate policy concept is to avoid “leakage,” whereby strict climate policy to reduce emissions in one region causes increases in another. A recent comparison of the dairy sectors of the Netherlands, California and New Zealand documents that California dairies on average produce more milk per cow than dairies in the Netherlands, and more than 2.6 times as much as dairies in New Zealand, while operating under stricter environmental regulations . Currently, the Intergovernmental Panel on Climate Change recommends using a fixed emission factor for dairy operations that is based on gross energy intake, which does not take diet composition into consideration . Calibration of GHG models for California using dietary information will provide a more accurate basis for measuring progress than current IPCC values, and for assessing the potential benefits of different forage and feed practices on emissions.

Additionally, bio-markers of proliferation and cell health such as Pax7, MyoD, and Myogenin may be measured to improve the robustness of predictions and correlations across assays. None of these metrics will aid in optimization if a sufficient model of the relationship between cell growth, media cost, and overall process cost is not considered. Therefore, a techno-economic model of the process is needed to tie together the large-scale production process to bench-top measurements. Secondly, further “white-box” studies that focus on the meta bolomics of the cell lines would be very useful in defining the upper / lower bounds and important factors of these DOE studies. Developing robust cell lines adapted to serum-free conditions would open up the design space for use in DOE studies because very poorly growing cells are difficult to optimize in DOE studies. In general, white-box or traditional studies act to constrain the complexity of future DOE studies, so must be conducted in collaboration with DOE. Experimental optimization of physical and biological processes is a difficult task. To address this, sequential surrogate models combined with search algorithms have been employed to solve nonlinear high-dimensional design problems with expensive objective function evaluations. In this article , a hybrid surrogate framework was built to learn the optimal parameters of a diverse set of simulated design problems meant to represent real-world physical and biological processes in both dimensionality and nonlinearity. The framework uses a hybrid radial basis function/genetic algorithm with dynamic coordinate search response, utilizing the strengths of both algorithms. The new hybrid method performs at least as well as its constituent algorithms in 19 of 20 high-dimensional test functions,plastic pots for planting making it a very practical surrogate framework for a wide variety of optimization design problems.

Experiments also show that the hybrid framework can be improved even more when optimizing processes with simulated noise.The design and optimization of modern engineering systems often requires the use of high-fidelity simulations and/or field experiments. These black box systems often have nonlinear responses, high dimensionality, and have many local optima. This makes these systems costly and time consuming to model, understand, and optimize when simulations take hours or experiments performed in the lab require extensive time and resources. The first attempt to improve over experimental optimization methods, such as ‘one-factor-at-atime’ and random experiments was through the field of Design of Experiments . Techniques in DOE have been adapted to many computational and experimental fields in order to reduce the number of samples needed for optimization. These methods often involve performing experiments or simulations at the vertices of the design space hypercube. Full-Factorial Designs are arguably the simplest to implement, where data is collected at all potential combinations of parameters p for all levels l requiring l p samples in total. Even when l = 2 the number of experiments or simulations quickly becomes infeasible so Fractional-Factorial Designs using l p−k experiments for k ‘generators’ are often used to reduce the burden. While such designs are more efficient, they have lower resolution than full designs and confound potentially important interaction effects. Therefore, DOE techniques are often combined with Response Surface Methodology to iteratively move the sampling location, improve model fidelity as more data is collected, and focus experiments in regions of interest. Stochastic optimization methods such as Genetic Algorithms , Particle Swarm Optimization, and Differential Evolution have also been used to explore design spaces and perform optimization on both simulated and experimental data, often requiring fewer experiments than traditional DOE-RSM techniques. The quickly developing field of surrogate optimization attempts to leverage more robust modeling techniques or Kriging / Gaussian Process models to optimize nonlinear systems.

They often employ a stochastic, uncertainty-based, or Bayesian search algorithm to intelligently select new sample points to query for experimentation or simulation. Due to the variety of modeling techniques and search algorithms available, hybrid algorithms, which attempt to leverage each methods strengths, have proliferated. These hybrid approaches usually involve taking ensembles of surrogate models and asking each surrogate for its best set of predicted query points. New queries are then conducted at these points, often weighted in favor of regions/surrogates with low sample variance or optimal response values. The drawback of many of these algorithms is that they are not always generalizable to design problems of diverse dimensionality and nonlinearity. A surrogate optimization algorithm is presented here, which uses an evolving RBF model and hybrid search algorithm. This search algorithm selects half of its query points using a Euclidean distance metric truncated to provide diversity in suggested query points. This is based on a neural network genetic algorithm developed for bio-process optimization, which has been shown to be more efficient than traditional DOE-RSM methods . The other half of the query points are selected using a dynamic coordinate search for response surface methods algorithm based on work developed for computationally expensive simulation.The performance of the NNGADYCORS hybrid algorithm is tested against NNGA and DYCORS separately. Further evaluation is performed to probe potentially useful extensions of the hybrid algorithm to address simulated experimental noise, to improve algorithm convergence over time, and to address cases in which certain groups of parameters have a greater influence on the response values than others. The NNGA algorithm is based on a RBF-assisted GA. The NNGA uses an RBF model to suggest points that are close to but not directly on top of optima, using a truncated genetic algorithm .

One advantage that GAs have over gradient-based methods is that their randomness allows them to efficiently explore both global and local regions of optimality. This makes them very attractive for an optimization framework attempting to look for global optima while facing uncertainty associated with a sparsely explored parameter space, and thus untrustworthy RBF models. This framework is shown in Figure 2.1 and the TGA is illustrated in Figure 2.2. First, a database of inputs X and outputs Y of No total queries is collected . An RBF model is constructed using the training regime discussed in Section 2.2.1. Next, a TGA is run using a randomly initiated population of potential query points with the goal of minimizing the RBF predicted output. In each iteration of the TGA, queries expected to perform the best survive a culling process and have their information propagated into the next iteration by a pairing, crossover and random mutation step. After each iteration, the best predicted query is recorded. When the average normalized Euclidean distance between the TGA’s current predicted best query and its next N −1 predicted best queries, dav,norm, is less than or equal to the critical distance parameter CD = 0.2,drainage for plants in pots the TGA is considered to be converged and submits this list of N best points for potential querying . This TGA is run a total of kmax = 4 times, and its query selections from all rounds of TGA queried to give the next set of data for simulation or experiments.The NNGA-DYCORS algorithm was tested against its constituent algorithms, NNGA, and DYCORS individually. Examining the performance of the constituent algorithms , the NNGA algorithm consistently works well in high dimensions , while the DYCORS algorithm performs better in low dimensions . This was the case both over time and at the final optimal query points . Given these differences in performance, it stands to reason that a hybrid approach would provide a sensible route to a more robust algorithm that could be used on a wider variety of dimensions. As seen in Figure 2.3, the hybrid NNGA-DYCORS often outperforms or performs similarly to the next best constituent algorithm in each experiment. This is reinforced by the data in Tables A.1 and A.2, where the final optimum of the hybrid NNGA-DYCORS is lessthan or equal to the final optimum of the next best constituent algorithm in 19 of 20 experiments . An optimum may be considered better if its upper bound is less than the mean of another algorithm’s optimum.

While this is a rough approximation of the comparative performance of the algorithm, it strongly indicates that the NNGA-DYCORS is robust on a wide variety of problem sets and dimensions. In intermediate cases , the NNGA-DYCORS continued to outperform or perform as well as its most competitive constituent algorithm, showing its usefulness in design optimization problems where it is not obvious a priori what dimensionality counts as ’high’ and ’low’. To test the effect of random noise on the ability of the surrogate optimization algorithms to find optimal parameters, a random noise ewas added to the output of the simulation. It is common practice, especially in noisy, low-data, and data-sparse models, to improve the out-of-sample generalizability by model selection procedures such as cross-validation to avoid overfitting. To address the issues with stochasticity in these experiments this, a hyperparameter optimization loop for the number of nodes nnodes in the RBF model was added to the NNGADYCORS algorithm, where cross-validation over the database was used to select the optimal nnodes. In this case we deliberately trade higher bias for lower variance to reduce overfitting. As can be seen in Figure 2.4, application of a node optimization scheme improved the learner’s performance over the regular scheme in nearly all cases. It should be noted that in these experiments, the linear tail of the RBF was excluded, so Equation 2.3 was modified to be Φλ = Y and was solved. There is a seemingly infinite number of modeling techniques, search optimization algorithms, and initialization/infill strategies in the literature to facilitate optimizing expensive objective functions. However, the characteristics of the experimental system and design space are never really known a priori, so having an algorithm that is more efficient than traditional methods and able to work with a wide variety of problems is advantageous. Therefore, the goal of this article was to develop a surrogate optimization framework that could be successfully applied to test problems with a wide range of dimensionality and degrees of nonlinearity. The NNGA-DYCORS algorithm runs two surrogate optimization algorithms in parallel. The NNGA uses a Euclidean distance-based metric to truncate a genetic algorithm, whose best members are k-means cluster distilled into a final query list. This acts as a global optimization process because the internal genetic algorithm searches over the entire design space. The DYCORS algorithm perturbs the best previous queries using a dynamic Gaussian distribution, where the perturbations are adjusted based on cumulative success and the total number of queries in the database. Thus, DYCORS acts as a local search method in the region defined by a Gaussian centred at its best queries. Both arms of the hybrid algorithm use an RBF for prediction.The result was that the NNGA-DYCORS hybrid algorithm was statistically equal to or outperformed its constituent algorithms in the 19 of 20 test problems. This demonstrates the robustness of the NNGA-DYCORS, as it performs as a best case scenario on a variety of test problem dimensions and shapes. This is important because, in real experimental problems, one does not know the shape of the surface a priori, highlighting the utility of a generalizable optimization framework such as the NNGA-DYCORS. In addition, it is never clear what constitutes a ‘high’ and ‘low’ – dimensionality design problem, so an algorithm that performs well in arbitrary dimensions should have large practical value. The DYCORS algorithm was already shown to be competitive compared to other heuristics, and the NNGA was demonstrated to be significantly more efficient than traditional experimental optimization methods. It stands to reason that this hybrid framework should extend the usefulness of both algorithms to test problems of arbitrary dimensionality and degree of nonlinearity. Using a node optimization scheme to reduce model variance during query selection improves hybrid algorithm performance, especially for noisy surfaces . Practitioners should therefore consider built-in regularization to avoid overfitting of the data when dealing with expensive, data-sparse and noisy systems. Optimizing the number of nodes was specific to this RBF variant, but the optimization loop in Section 3.2 could be applied to any model hyperparameter. In the next set of experiments, the method of making the NNGA-DYCORS convergence parameters dynamic during query selection did not improve performance. This indicates that it may not be fruitful to pursue extensive algorithm parameter adjustments/heuristics for this algorithm, and there is little sensitivity in the selection of algorithm convergence parameters on the outcome, unlike the results in previous articles on the subject. Finally, to mimic typical engineering scenarios where response sensitivity varies with the inputs, the test functions were scaled with a sensitivity vector.

Fertilizer use on the different crops across agro ecological zones is presented in Annex 2. Both adoption and application rates are higher in the zones with higher agro ecological potential than in lower agro ecologically potential zones. This may indicate that fertilizer use on the crops is more profitable and less risky in higher agro ecologically potential zones.The marginal effects of the Probit model show changes in the probability of adoption of fertilizer for additional unit increase in the independent variables. Farmers with no formal education had 7.6% less probability of adopting fertilizer compared to those with primary education, while farmers with secondary and post-secondary education respectively had 11.2% and 16.5% higher probability of adopting fertilizer than their counterparts with primary education. Educated farmers can better process information more rapidly than otherwise . It can also be presumed that educated farmers have a higher level of awareness of the benefits of fertilizer use in agricultural production.Having received credit increased probability of fertilizer adoption by 12.9%. This suggests that relaxing liquidity binding constraints among smallholder farmers through access to credit will significantly increase their probability of adopting fertilizer. Growing a cash crop is associated with 15.3% higher probability of fertilizer adoption. The major cash crops considered here have credit schemes that guarantee farmers’ input acquisition on credit,round pot which is repaid through deductions from the produce which farmers sell through the commodity cooperatives or factories. This indicates the important role of credit and guaranteed markets in promoting fertilizer adoption.

The probability of adopting fertilizer decreases by 0.7% for every kilometre increase in the distance to fertilizer seller. It is noteworthy that the distance to the nearest fertilizer seller declined from 8.1 km in 1997 to 3.4 km in 2007, which could be a result of improved input delivery systems after liberalization . Agro ecological potential significantly influences fertilizer adoption. Households in the drier and lower agro ecologically potential zones had between 50.8% and 77.2% lower probability of adopting fertilizer compared to those in the High potential maize zones, an indication that profitability of fertilizer use as dictated by ago ecological conditions has a significant impact on adoption. Compared to the Central highlands, an equally high potential region, probability of fertilizer adoption in the High potential maize zone was lower by 6.6%.Determinants of fertilizer use intensity conditional on adoption are presented in Table 6. Having no education at all or secondary education compared to having primary education no longer plays significant role in fertilizer use intensity. The significant determinants of fertilizer use intensity are gender, post-secondary education, household size, dependency ratio, credit, growing of cash crop, distance to fertilizer seller, distance to extension and agro ecological potential. The marginal effects show that for an additional year of age, fertilizer use intensity declines by 0.12kg/acre. A household being male-headed is associated with 6.6 kg of additional fertilizer per acre. Compared to primary education, post-secondary education increases fertilizer application rate by 4.5kg/acre. A unit increase in the household size increases fertilizer application rate by 0.49kg/acre. This is plausible as households will strive to enhance their food security status by trying to increase yield levels. At the means, a unit increase in dependency is associated with a reduction of fertilizer use intensity by 2.5kg/acre.Conditional on a household using fertilizer, receiving credit increases fertilizer application rate by 16.2kg/acre, while growing a cash crop increases fertilizer application rate by 19kg/acre.

A one kilometre increase in the distance to an extension service reduces fertilizer application rate by 0.8kg/acre. Paradoxically, unlike in the Probit model where distance to fertilizer seller negatively and significantly influenced fertilizer adoption, an increase in the distance to fertilizer seller positively and significantly influences fertilizer use intensity. This is a puzzling issue that may need further investigation.Fertilizer is considered one of the most important inputs for the achievement of increased agricultural productivity and food security in Kenya and, in deed, SSA. Although Kenya has registered high rates of fertilizer adoption, raising the intensity of use remains a key challenge. The patterns in households’ fertilizer use showed dramatic rise in adoption in the last decade. Fertilizer application rates, however, showed marginal increase over the period. Fertilizer use in the drier agro ecological zones is still way below that in the higher agro ecologically potential zones. This may be associated with higher risk involved in and lower profitability of using fertilizer in the drier areas. The relatively higher fertilizer use in higher agro ecologically potential zones may also be influenced by the presence of major cash crops such as tea, sugarcane and coffee, which have organized input credit schemes which allow farmers to acquire inputs on credit and repay through deductions made on deliveries of the produce. Econometric analysis has shown that age, education, credit, growing cash crop, distance to fertilizer market and agro ecological potential are statistically significant in influencing the probability of adopting fertilizer. On the other hand, the strongest determinants of fertilizer use intensity were gender, household size, dependency ratio, credit, growing cash crop, distance to extension services and agro ecological potential. Increasing fertilizer use intensity in general and promoting fertilizer adoption in drier areas of Kenya require several interventions. First, there is need for relaxation of credit constraint through improved access to agricultural credit for especially low income farmers who depend on food crops and do not have access to credit opportunities offered under cash crops’ input credit schemes. Another way of relaxing credit constraint would be to improve access to viable off-farm income generating activities.

Existing literature suggests positive spill over effects of off-farm income on agriculture by substituting for credit when credit markets fail . Secondly, concerted efforts to promote fertilizer use among farmers in the drier areas cannot be overemphasized. Extension efforts combined with fertilizer distribution innovations would ensure that farmers in these areas are sensitized on the benefits of using fertilizer for productivity growth. In addition, long term efforts are needed to establish and expand small-scale irrigation projects, which can help overcome the adverse effects of inadequate rainfall experienced in these areas. Finally,round plastic planter the liberalization of the fertilizer sub-sector has led to increased national consumption of fertilizer and Kenya has been a success case where the private sector has thrived relatively well. One of the current factors impeding fertilizer use is the high world fertilizer prices in relation to the output price for commodities . The world prices per ton of DAP increased from US$ 260 in 2007 to US$ 800 in 2008. If such trends continue, gains in fertilizer adoption and intensity of use over the last decade may erode. Efforts to reduce the costs of fertilizer delivery would help to offset the effects of rising world prices. Government can invest in rural infrastructure, efficient port facilities and standards of commerce to reduce the costs of distributing fertilizer.Twenty-five years after the publication of the first IPCC Assessment Report, it is instructive to step back and ask what we have learned about the economic impacts of climate change to the agricultural sector, not just from a technical standpoint, but from a conceptual one. California is an ideal focus for such an analysis both because of its strong agricultural sector and proactive climate policy. After passing the 2006 Global Warming Solutions Act, the state has sponsored research to complete three climate change assessments, with the fourth assessment report in progress at the time of submitting this paper. This effort to study adaptation appears to be relatively more prolific than in many other global sub-regions, particularly over the past decade . Assessing adaptation potential — the institutional, technological, and management instruments for adjusting to actual or expected climatic change and its effects — represents an important turning point in the climate impacts literature. The important role of responsive decision-making by farmers and institutions is recognized for the first time as the key ingredient to dampening the effects of climate change . Adaptation was simply mentioned as an optimistic afterthought in earlier studies, which suggested that agriculture would fully or mostly adjust in the long term — although there was sparse detail on how it would do so . When adaptation was directly included in the modeling framework, economists found that the estimated welfare damages from climate change documented in previous studies declined . In colloquial terms, this is a shift from modeling the “dumb” farmer to modeling one with reasonable economic agency. There are four key concepts linked to the idea of adaptation: vulnerability, adaptive capacity, economic welfare, and economic efficiency. In the IPCC literature, adaptation is connected to the foundational concept of vulnerability, defined as the propensity for agricultural systems to be affected by future climatic changes . Vulnerability can also be defined endogenously as the ability of farmers and institutions to respond and adapt to, and recover from such changes .

This latter definition is synonymous with the concept of adaptive capacity, or the ability of a system to moderate potential damages and take advantage of adaptation and mitigation opportunities to reduce vulnerability of the system to climatic changes . Economic welfare is the sum of producer and consumer surplus in the agricultural sector. Adaptation dampens welfare losses caused by climate change. The relationship of adaptation with vulnerability is more complex, and better represented as that of trade-offs. For example, changing the crop mix in favor of high value crops may reduce vulnerability to water scarcity, but it may increase vulnerability to heat tolerance. Finally, the concept of efficient adaptation has been defined as a situation where the costs of effort to reduce climate-induced damages is less than the resulting benefits from adapting . Given the central role of farmer and institutional responsiveness, how do recent agro-economic assessments suggest that specific adaptations may improve economic welfare and reduce vulnerability? What is economically efficient adaptation in the short and long-run? What are the limits to the agricultural sector’s adaptive capacity? This is certainly not the first review of climate impact assessments to California agriculture. Smith and Mendelsohn highlighted the importance of regional climatic impacts to several economic sectors in California , integrating across range of modeling approaches . The agricultural impacts are calculated by the Statewide Agricultural Production model under wet and dry scenarios. The results echo those of more recent SWAP studies, suggesting that field crop usage will decline by the end of the century under a dry scenario, though the decline in revenues will be partially offset by increased production of high-value crops. Prior to Smith and Mendelsohn , several notable studies examined the state of the knowledge of climate assessments at the US level . In particular, Lewandrowski and Schimmelpfennig integrate the knowledge from both programming and econometric studies of the agricultural sector. Other reviews have focused on the technical details of the different modeling approaches without discussing the results of the various studies . Following the pioneering work of Smith and Mendelsohn , this paper also focuses on California. The state is a leader in agricultural production, with $53.5 billion in sector cash receipts in 2014. California accounts for roughly 2/3 of US fruit/nut production, and 1/3 of US vegetable production . Roughly 1/3 of California cropland, or 9 million acres, is irrigated , making the state’s agricultural sector highly vulnerable to changes in groundwater and surface water supply . Several programming and econometric studies have been published after Smith and Mendelsohn , that operationalize the concept of adaptation . This paper begins with a review of regional impacts of climate change to California agriculture. It is followed by a review of the results from recent programming and econometric studies. The final section synthesizes the results from these studies, addressing lessons learned about vulnerability,adaptation, and adaptive capacity; and how these relate to economic welfare and efficiency.Observational studies indicate that average daily temperature and daily minimum temperatures, particularly during the winter season, have increased in California . Average daily temperature in the US Southwest for the previous decade has been higher than any decade observed in the previous century . Barnett et al. find that daily minimum temperatures in winter have increased between 0.28– 0.43 C per decade from 1950–1999. Not just magnitude, but an increased rate of warming has been observed. Karl et al. suggest that the US Southwest has experienced the most rapid rate of warming in the nation. Observed precipitation patterns are fundamentally more complex and variable than temperature, exhibiting a high degree of variability across space and time.

The OECD defines agricultural support as “the annual monetary value of gross transfers to agriculture from consumers and taxpayers, arising from governments’ policies that support agriculture, regardless of their objectives and their economic impacts.” The PSE, then, “represents policy transfers to agricultural producers…and is composed of market price support, budgetary payments and the cost of revenue foregone by the government and other economic agents” . As the data below reveal, the paradox of farmers continuing to receive robust economic aid despite limited employment and negligible contributions to the national economy holds true beyond Europe.Farmers in Japan have enjoyed great success in imposing their policy preferences due in part to their homogeneity and highly organized representative associations. Small farmers dominate the agricultural sector, which makes it easy for farmer associations to promulgate a single, coherent message. In addition, a strong union that is well organized nationally, regionally, and locally, represents Japanese farmers. Finally, unlike Europe and the United States, there is little if any pressure from sectoral organizations. The main farming organization in Japan is Japan Agriculture, referred to as JA or Nōkyō. The JA is a three-tiered organization, with national, prefectural, and local-level cooperative groups. The JA commands near universal membership of the Japanese farming community in large part due to the services and benefits it offers. It claims to have nearly 10 million members . Its main businesses are banking, insurance, agricultural retail and wholesaling, and supply of farming materials. In addition to these benefits and services, which are not uncommon among agricultural cooperatives,10 liter drainage pot the JA’s scope of business includes real estate, travel agencies, supermarkets, and even funeral homes .

Essentially, “within the villages, the JA is a one-stop service. Farmers and everyone else in the village use JA services” . An LDP politician explained that the JA has far-reaching influence and is a cornerstone of rural society, with even non-farmers depending on the JA for services, “No other organizations in Japan are like the JA with so much local organization and influence. The JA is crucial in local community because of the infrastructure it provides. As a result, even non-farmers in rural areas need and depend upon the JA” . Ultimately, this wide range of services means that the JA can forge a relationship with farmers and the broader rural community that extends beyond just agriculture. Indeed, the JA can assist rural communities in all their needs, even those that come after death. Along with high membership levels, much of the JA’s power derives from the fact that it has been in an official corporatist relationship with the state since it was formally created via legislation in 1947. This close relationship with the state has been quite beneficial for the JA, with the government at times heavily regulating and protecting the JA’s banking and insurance businesses, even going so far as to bail out JA banking multiple times, both after 1980s economic bubble burst and again in 2008. For example, Norin-Chukin a major agricultural cooperative bank had invested extensively in real estate during the 1980s boom. When the bubble burst and the real estate market collapsed, JA affiliated banks, Norin-Chukin chief among them, sustained heavy losses. As a result of political lobbying, the JA was able to reach an agreement where it was only responsible for ¥530 billion out of a total of ¥5.5 trillion in losses . The state has also granted the JA exceptional status in antitrust law, which has afforded the JA monopolies on the supply of agricultural inputs to farmers . Further exceptions are made for the insurance wing of the JA, “which is allowed to sell multiple kinds of insurance whereas other firms are traditionally limited to providing only one type of insurance” .

As these examples suggest, farmers and the JA have been quite successful in their efforts to influence agricultural policy making. An important area of success for Japanese farmers has been in shaping Japan’s trade negotiations, pressing for protectionism even when other groups seek greater trade liberalization. In these negotiations, Japanese agriculture is able to impose its preferences despite pressure from the Japanese business lobby, Keidanren, which stands to gain far more from liberalization than agriculture would ever lose. These victories for Japanese farmers have come at both the GATT/WTO and in Japan’s bilateral trade agreements. The GATT Uruguay Round sought to reduce if not eliminate agricultural subsidies and remove tariffs and trade barriers in an effort to liberalize agricultural trade. In these negotiations, Japan’s position was largely defensive and was grounded in a desire to make as few concessions as possible. Its objectives were shaped primarily by the special position of rice producers and also by the overall high level of protection of agriculture. The LDP, whose political position was vulnerable at the time, promised farmers that no amount of foreign rice would be allowed to enter the domestic market . Fundamental incompatibility between GATT objectives and the policy preferences of major negotiating parties, including Japan and the European Community, resulted in the round grinding to a halt. In the end, although reducing tariffs was a major goal of the negotiations, a modification was negotiated specifically for Japan to allow it to delay tariffication of rice in exchange for accepting more imports of agricultural products, but only in sectors that were unimportant to Japanese agriculture such as dairy production. In addition, farmer subsidies were protected, despite the GATT UR goals of eliminating them. By the end of the GATT UR negotiations, Japanese farmers walked away with an agreement that protected their core commodities and allowed them to largely avoid the removal of tariffs for key products, while also maintaining a system of income support for farmers. In September of 2003, Japan was in the final stages of a free trade agreement with Mexico, which had been delayed by agricultural opposition.

Frustrated with the delays, Prime Minister Junichiro Koizumi ordered his trade negotiators to “get it done” . In the end, a tripartite coalition of agricultural representatives was able to extract considerable concessions for agriculture that finally allowed the agreement to move forward. The concessions included a reduction in the level of tariffs that had to be removed and special protection arrangements for “politically sensitive” commodities including pork, beef, chicken, oranges, and orange juice . Although this free trade agreement was concluded with Mexico, agriculture continued to block any progress on other free trade agreements Japanese officials desired at the time with the Philippines, Thailand, and South Korea. One major reason Japanese farmers have been so successful in pushing their policy preferences and forestalling liberalizing trade agreements is that the main groups in other countries that traditionally challenge farmers by supporting liberalization,25 liter pot namely consumers, business, and the food industry, are either unwilling or unable to challenge the JA’s preference for protection . Moreover, while farmers are united in their opposition to agricultural liberalization their opponents, most notably consumers and the food industry, are internally divided. Consumer organizations, for example, are dominated by concerns over food quality and prefer to restrict access to the Japanese market to ensure that the preponderance of available products are those of Japanese origin, in which they have a high degree of trust. Because of this strong preference among consumers for food of Japanese origin, many in the food processing and distribution industry are reluctant to push for agricultural liberalization. Their fear is that demand for their products will decline if they are made with or include the imported agricultural goods. The result is that, despite their small share of the population, farmers are able to extract new policies, or preserve existing policies, that benefit a small share of the population and inconvenience a much greater share of the population. While these group preferences are indicative of the peculiarities of the Japanese case, the broader explanation of farmer influence and power tracks the European story. Japanese farmers, like those in Europe, have powerful and well-coordinated organizations. These organizations operate from the national level all the way down to the local level, giving farmers access to and influence over key actors at all levels of decision making. Tight control over members and impressive capacity for coordination allows Japanese farmer organizations to influence not only politicians concerned with re-election but also key actors, like business, that might challenge farmer preferences. Boycotts are one common strategy employed by farmer organizations in Japan to shape policy by punishing other interests that challenge agriculture. For example, in the mid-1980s, Nōkyō led a boycott against Mitsubishi Kōgyō Cement because a company executive belonged to a Nikkeiren committee that pushed for agricultural policy reform. Since that incident, Nikkeiren has struggled to find executives willing to sit on the committee . Farmers also executed a successful boycott of Sony, Daiei , and the food-maker Ajinomoto because their executives had pushed for agricultural policy reform as part of a Keidanren committee. In these cases, the boycotts were ended only after the executives from the offending companies apologized to farmers and quit the committee . So, farmer organizational power in Europe is often manifested through street protests, Japanese farmers often direct their organizational influence toward hurting the economic interests of their main policy opponents.

The JA’s organizational strength allows Japanese farmers to exert significant electoral influence, rewarding politicians who commit to protecting and advancing preferred farmer policies, and punishing those who do not. As a Japanese official explained, “If JA doesn’t like a candidate, they will do a smear campaign. Farmers are maybe not strong enough to make someone win, but they are strong enough to make sure someone loses” . The farmers have long been a staunch ally of the Liberal Democratic Party , which governed Japan, uninterrupted from 1955 to 1993. Since its formation in 1955, the LDP has only been out of power from 1993 to 1994 and 2009 to 2012. The JA’s ability to coordinate the voting of its membership played an important role of the LDP’s defeat in 2009 and its return to power in 2012. In the run up to the 2009 election, many farmers threw their support behind the Democratic Party of Japan as opposed to their traditional ally, the LDP. This shift appears to have been prompted more by the DPJ’s aggressive campaign to win the farmers over than farmer anger with a specific LDP policy. In an effort to win farmers away from the LDP, the DJP announced a plan to transition agricultural policy from price supports to a system of direct income compensation. The policy was an extension of one offered in the 2007 Upper House elections that proved to be very successful in winning rural votes away from the LDP. These policy promises in 2009 lured numerous JA prefectural offices into tempering their commitments to the LDP, saying that decisions on whom to support would be made on a district-by-district basis, or, in the most extreme cases, that this election would be a “free vote” and no official candidate would be endorsed. The DPJ, thanks to their plan for supporting farmer incomes, won the support of the agricultural community and thus the election. After taking power, the DPJ adopted their new farmer income scheme, which provided a direct income subsidy for all commercial farm households, regardless of size. The scheme was also designed to compensate farmers for times when production costs exceeded sale prices. Under the policy, farmer incomes increased for the first time since 2003. Despite these positive developments, the LDP took back farmer support and won the next elections in 2012. A central promise of the LDP was to increase public spending on the farm sector, which had been cut by the DPJ to pay for the new income support program. Under the DPJ, the budget of the Ministry of Agriculture, Forestry, and Fisheries had declined . In the run up to the 2012 elections, the LDP committed to reversing this decline. In addition, its agricultural policy platform promised to replace the “individual farm household income compensation scheme with enhanced direct payments to farmers for the multifaceted functions of agriculture” . The way the direct income payment was handled by the DPJ also came under criticism: some viewed it as a way to separate farmers from the powerful farmer organizations by weakening the dependent relationship between the two.

The second group was the anti-reform alliance consisting of France, Germany, Ireland, Italy, and Spain. These countries took issue with nearly every aspect of the reform package, in particular decoupling and modulation. Germany, with large farms in the east and highly efficient farms in the west, opposed a limit being placed on total CAP payments. Both of these sets of farmers would be adversely affected by a limit on the total payment a farmer could receive. German farmers in both the east and west were already receiving more in direct payments than the proposed payment cap would allow. These member states also opposed the timing of the reforms, arguing that Agenda 2000 should be fully implemented before any further reforms were adopted . France’s position became even more staunchly anti-reform after a leftist cabinet was replaced by a center-right government in 2002, and Hervé Gaymard, a member of Chirac’s own party, was installed as minister of agriculture. Several agricultural lobbies posed three main reform critiques of their own. The lobbies argued that the new system of payments would not allow farmers “in the least-favoured regions, where low productivity and lower competitiveness” predominates to earn a livable income . The result, they argued, would be land abandonment and an increase in unemployment. Second, they voiced the concern that paying farmers regardless of production would negatively affect public opinion and could ultimately result in the complete termination of direct payments to farmers . Third,vertical farm tower the proposal to base the direct payment on historical yields would serve to perpetuate past discrimination in favor of certain products, producers, and regions .

The third group represented those countries in the middle that, while not completely opposed to the reforms, had some specific objections. Countries in this group were Austria, Belgium, Greece, Finland, and Luxembourg . Finland and Austria were traditionally protectionist agricultural countries and thus supported subsidies as a means to help their farmers. However, because Austria and Finland each had an agricultural sector that was predominantly small-scale and high value added, they favored strategies for rural development, greening, and multi-functionality, as opposed to production-based subsidies that favored large scale cultivation of commodity crops . At a meeting of the Council of Ministers on 8 April 2003, decoupling was discussed for the first time. Only the UK, the Netherlands, Sweden, and Denmark expressed support for Fischler’s proposal to completely disconnect payment from production . Most of the other member states preferred partial decoupling, whereby a portion of a farmers’ income payment would continue to be linked to how much he or she produced, but no member state offered any concrete ideas or proposals for how partial decoupling could be carried out . While many countries were neither fully opposed nor fully in favor of the reform, no agreement could be reached without breaking the French-led blocking minority. Under the rules of QMV, a blocking minority consisting of a minimum of 4 countries that represented at least 35% of the population could prevent the passage of a proposal. Given the existence of this blocking minority, member states in the middle had no incentive to officially back reform, particularly since their formal support might provoke the ire of the farming community at home. There was no incentive to express support or even negotiate on the terms if the blocking minority could thwart the whole package. Though the Commission preferred to pass reforms with unanimous support, with the continued expansion of the EU, it was no longer feasible to pass reforms only with unanimous support.

The adoption of QMV facilitated a faster negotiation process than was possible under unanimity rules, and ensured that a single country could not use a veto to stymie reform. Ireland ended up abandoning the anti-reform group early. Irish farmers’ unions opposed the reforms, but their members did not. The farmers supported the reforms because they felt they would provide them with adequate income support while also giving them the freedom to farm a greater diversity of crops . Even without Ireland, however, the other four countries, France, Germany, Italy, and Spain, could form a blocking minority on their own under the rules of QMV. In order to break this minority alliance of France, Germany, Italy, and Spain, Fischler targeted the Spanish delegation, as it was believed that “Spain joined the French to gain some breathing space” rather than because of outright objection to the reforms . Fischler asked British Prime Minister Tony Blair to reach out to Spanish Prime Minister Aznar . Spain was a crucial country to flip, because it would break the blocking minority led by France. Blair agreed but asked Fischler to drop the capping of direct payments in exchange. These caps, which would be applied primarily to big farms, would hit the UK especially hard . Fischler agreed and Blair began working with Fischler to swing the other member states in support of reform. One of Spain’s central demands was to amend the decoupling proposal to allow for partial decoupling in certain sectors, at the member states’ discretion. Partial decoupling would allow the Spanish government to continue allocating a percentage of income payments based on production in sectors important to Spain, namely sheep and goat farming. Once that concession was made, Spain shifted in favor of the reform. With the blocking minority broken, France and Germany quickly followed suit, hoping to grab some concessions in exchange for their support of the reform Similar to Spain, Germany and France also received a concession that allowed them to keep a certain percentage of income payments coupled to production for sectors of importance.

The French switch was also motivated by pressure from the Association Générale des Producteurs de Blé , the cereals division within the FNSEA. Chirac’s opinion was strongly influenced by that of France’s national farming union, the Fédération nationale des syndicats d’exploitants agricoles , with some Commission officials describing Chirac as “entirely beholden” to the FNSEA . Chirac completely opposed decoupling until he was approached by AGPB leaders, who told him that they supported the policy change . The cereals farmers reasoned that Fischler’s reform, with cuts to price supports being compensated for by direct income payments,vertical plant tower was far better than the uncertainty of an unreformed CAP. They feared that if left unreformed, the CAP would be subject to dramatic price cuts in the future to bring it into alignment with budgetary standards, and that no compensation would be offered for the price cuts. In addition, given that the French cereals sector was highly efficient and competitive independent of inflated prices, they believed that the new system would allow them to conquer additional market share. Farmers from other member states would be less competitive without inflated prices to support them. In exchange for its support for the reform, Germany was able to secure a concession that allowed for the SFP to be based on a regional calculation, as opposed to historic production receipts. The EU’s proposed historical method of calculation tended to perpetuate past inequalities across products, producers, and regions . Under Germany’s regional model by contrast, all farms in a region would be eligible to be paid the same amount, regardless of what they had produced in the past. This model was preferred by Germany in large part because of internal diversity in its farming community. Of course, there was a large gulf between the west and the east, but more importantly there was diversity within the same region depending on the type of farming undertaken and the location of a farm within a given region. The regional model, then, would eliminate the inequalities in payment perpetuated by the historical model and ensure that all farmers in a given region were paid the same. The calculation for payments under the regional model was based on all eligible hectares of agricultural land in the region. This method allowed both grassland and arable land to be included in the calculation, potentially increasing the amount of support included in the financial envelope for each region. After calculating the amount each region was entitled to, member states using this calculation method could, if they wanted, move money from one region’s financial envelope into the envelope of another region. For example, the government had the option of redirecting some of the money owed to farmers in the most fertile regions, such as Bavaria, to farmers in areas that would earn far less under the regional calculation, such as those farmers in the difficult to cultivate lands around the Alps and to the large but inefficient farms of the East. This modification of the regional calculation method was intended to help counties address disparities in farmer incomes within their country.

France, Italy, and Spain also extracted amendments to the decoupling proposal allowing member states to avoid full decoupling in certain sectors if the member state believed that “there may be disturbance to agricultural markets or abandonment of production as a result of the move to the single payment scheme” . In other words, if countries feared that the transition to full decoupling might result in many farmers abandoning their land or would “disturb agricultural markets”, a vague phrase, left open to interpretation, they could avoid the transition to full decoupling. This concession essentially allowed member states to protect nationally important or favored sectors. The sectors where partial decoupling was permitted included: cereals and arable crops, sheep, goats, suckler cows, and slaughtered cows. In the end, the reforms passed with the support of every country but Portugal, which still wanted a higher milk quota . The final agreement on the MTR achieved Fischler’s goal of implementing the reforms necessary to save the CAP. The Single Farm Payment changed the way farmers received income support, weakening the link between these payments and production. By implementing this reform, the CAP would be able to continue to function once the new member states were fully incorporated in the CAP income payment scheme. The level of production in the current EU was already financially unsustainable if support was coupled. Adding the new member states, with a larger percentage of the population employed in agriculture and higher levels of production, to the existing system would explode the CAP budget. The final agreement also included modulation and cross-compliance, two programs intended to strengthen the environmental objectives of the CAP, addressing public dissatisfaction over unsafe food, agricultural pollution, and inequalities in CAP spending. Modulation re-directed a percentage of a member state’s income support funds into programs that supported rural development and environmental objectives. Some of the funds collected through the modulation system could also be re-distributed amongst the member states in an effort to correct inequalities in allocation of CAP support across the member states. Cross-compliance conditioned the receipt of income payments on meeting environmental standards. Despite their importance for the long-term survival of the CAP, these policies were only agreed to after many concessions and revisions were made to Fischler’s initial proposals. Table 5.1 highlights these concessions by comparing Fischler’s initial proposal to the final outcome.A new system of income support payments was agreed to, but payments were only partially decoupled from production and member states were offered numerous opportunities for exemption and delays in implementation across multiple sectors. Modulation was adopted, but at a much lower rate than Fischler hoped. As a result, little money would be directed toward environmental initiatives. Member states would also be allowed to keep a higher percentage of modulated funds than initially proposed, meaning that little redistribution among the member states would result from the program. Finally, new environmental standards were imposed under cross-compliance, but only at a lower level than initially proposed and with financial incentives attached to induce farmer cooperation. The discussion of the Fischler Reform in this chapter demonstrates four major claims in this dissertation. First, the case of the Fischler Reform, particularly when considered in comparison to Agenda 2000, illustrates how important disruptive politics, such as trade negotiations or enlargement, may allow for further-reaching reform than would otherwise be possible. Second, this case illustrates that even when other factors and influences combine to create an opportunity for major policy change, CAP reform still resembles the logic and process of welfare state retrenchment. The changes that were adopted are limited, are less dramatic and far reaching than initially proposed, and were often slow to take full effect.

The former sought to slash the CAP in an effort to reduce Germany’s EU financial contributions while the latter opposed spending cuts in an effort to protect German farmer benefits. France’s position was strengthened because an overall bi-partisan unity emerged from the divided government on matters related to the CAP. Despite these divisions, there was some agreement among the member states. The ministers broadly concurred that a reform should happen before the next round of enlargement, that the 1992 reform should be continued and extended, and that the intermediate strategy was the most favorable . More specifically, the ministers agreed that reform of the beef and dairy sectors was inevitable as at the time there were surplus problems with both of these sectors. However, they disagreed over the size of the cuts and degree of compensation. Germany wanted small cuts, while Sweden and the UK wanted cuts to be large. In terms of compensation, the UK and Sweden preferred to phase out compensation, while Austria, Finland, Germany, and Spain insisted on full compensation, and Greece, Italy, the Netherlands, and Spain claimed that compensation was discriminatory . Similar technical squabbles also broke out over how to handle the dairy and cereals sectors. Four other issues divided the EU member states. The first was dairy. The quota regime was set to expire in 2000. In order for it to continue, an agreement to extend it would have to be voted on by a qualified majority within the agricultural council. A majority of the member states, led by France and Germany,hydroponic vertical farming systems favored the Commission proposal allowing for the continuation of current price and quota policy on the grounds that it ensured a stable market and kept production in check.

These member states also recognized that the compensation that would have to accompany reform would push CAP spending beyond its limits. They argued that delaying the removal of quotas even further than the Commission had proposed, until 2006, would save the EU €8 billion in compensatory payments that it would not have to distribute if the system was left in place . The UK, Sweden, Denmark and Italy, however, all supported an end to the quota regime. They favored a more market-oriented dairy sector. Together, these four countries formed a blocking minority meaning that, if they stayed united, they could prevent a vote from passing under qualified majority rules. Problematically for this blocking minority, however, Italy was also a member of a group of countries, including Greece, Ireland, and Spain, that were willing to support the Commission’s proposal in exchange for an increase in their quotas . The Commission ultimately gave into the demands of Greece, Ireland, Spain, and Italy, offering them an increase in their dairy quota in exchange for their support, thus breaking the blocking minority. After Austria, Belgium, France, Luxembourg, the Netherlands, and Portugal argued that this quota increase was special treatment, the agreement was amended to increase the quotas for all member states, coupled with an additional specific increase for Greece, Ireland, Italy, and Spain. The remaining members of the now defunct blocking minority were promised only that dairy policy would be analyzed and evaluated as part of a mid-term review of the CAP, with the goal of allowing the quota system to expire after 2006. Unlike with dairy, the member states were largely in agreement that reform was needed for cereals and beef.

Efficient cereals farmers in particular were confident in their ability to compete on the world market, and also knew that they would fare better within the EU because they would not be losing market share to smaller and less efficient cereals farmers surviving on inflated prices. Beef producers would benefit from the declining costs of inputs from the cereals sector, once those prices were brought closer to world-market levels. For that reason, discussions concerned the level of cuts and compensation as opposed to whether or not reforms should occur at all. Germany, for example challenged the Commission’s cereal price forecasts, arguing that world cereals prices would soon rise to EU levels, rendering significant price cuts unnecessary. In addition, Germany and France supported price cuts for beef, but only so long as framers were offered full compensation. France, however, was firmly in favor of cereals price cuts. Unlike in Germany, French cereal farmers do not need to rely on price supports for survival; indeed, the French view these supports as exposing French grain farmers to unfair competition by “encouraging production in other regions which could not produce without price support” . During Agenda 2000, the FNSEA, dominated by the large grain farmers, had a particularly powerful ally in French President Jacques Chirac, described by one high-level government official as “the spokesman for the FNSEA” . The UK position on both beef and cereals was in line with their desire for greater market liberalization. More broadly, the UK remained opposed to essentially subsidizing the agricultural sectors and paying compensation to the farmers of other countries. For beef in particular, they asked that price cuts be increased to 30% and any compensation payments made temporary as opposed to permanent increases to the direct payment scheme . Ultimately, for beef the price cut was reduced from 30% to 20%. For cereals, the Agricultural Council agreed to keep the cut at the same 20% level, but to delay the full implementation with the cut being imposed in two steps instead of all at once. A buyout, increasing the beef premium, was needed to secure France’s support for the reform as well .

A third area of significant debate was the set of horizontal measures: cross-compliance, modulation, and payment ceilings. The countries with the largest farms, the UK and Germany, continued their staunch rejection of modulation or any ceiling on payments imposed. Their objection rested on the grounds that these policies “discriminate against large, efficient farms, thus undermining the objective of making European agriculture more competitive” . There was also widespread resistance to cross-compliance. The member states argued that they should decide environmental aims at the national level rather than having the EU attempt to set common environmental objectives for 15 member states, each with their own particular agricultural situations. In the end,vertical planting tower the Commission gave in to every major demand on the horizontal regulations: payment ceilings were dropped, modulation was made optional at the member state level, as was cross-compliance, and member states were allowed to determine their own environmental standards under the program. While these concessions may seem like a major loss for reformers, by including these policies in the reform, even if only optionally, Agenda 2000 reformers positioned their future counterparts to build on and extend the program, setting themselves up for systemic retrenchment in the future. These policies, while at this point not mandatory, had at least become part of the CAP system. The addition of these small, and seemingly unimportant optional new policies opened the door to deeper, structural changes in the future. A final area of debate concerned rural development and the drive toward further establishing the CAP’s second pillar. Austria, Finland, France, Portugal, Sweden, and the UK were all in favor of significantly strengthening the CAP’s second pillar. Despite a common preference for a stronger second pillar, these member states did not agree on what that should entail. Sweden, the UK, and to a lesser extent, Finland, advocated for a radical reform under which the second pillar would constitute the bulk of the CAP, with market measures and direct payments phased out over time. The others favored a more even distribution of spending between the two pillars while also working towards making the two pillars and their policies more complementary.

One notable way the Commission sought to more tightly join the two pillars was through cross-compliance whereby environmental standards, traditionally the domain of the second pillar, would be tied to direct payments, the purview of pillar one. France’s support for the second pillar marked a shift away from its staunch defense of the traditional CAP programs and was crucial in helping to secure increased financial commitments for rural development. The French government had recently adopted a new Loi d’Orientation Agricole and a major part of it, the Contrats Territoriaux d’Exploitation, was essentially targeting the same objectives as many of the rural development programs supported under the second pillar . Specifically, the new Loi d’Orientation Agricole was designed to preserve the smaller-scale family farms while also promoting high food quality standards and the preservation of the environment in rural and agricultural areas . An increase in funding for the second pillar would essentially allow France to cofinance its new domestic policy. In order to push forward progress toward reaching a final agreement and to illustrate the difference between reality and what the member states wanted the Commission distributed a table, within the Agricultural Council, that reported that if all of the outstanding demands of the member states were included in the final CAP agreement, the annual budget would be exceeded by €25 billion, or roughly 8% over the course of the six-year budgetary period . A compromise was reached and shortly thereafter, and the agreement was officially approved by the Agricultural Council. Importantly, this agreement was still subject to final approval by the European heads of state and government when the European Council met in Berlin a few weeks later to approve the entire Agenda 2000 package. The agreement reached by the Agricultural Council contained changes to the Commission’s proposal for all three of major sectors under discussion: beef, dairy, and cereals. For the beef sector, as a concession to Italy and France, a higher slaughter premium was approved. In addition, the price cuts were reduced from 30% to 20% and would take place in three stages, not one. Reforms for both dairy and cereals were delayed. Dairy reform would not begin until 2003 and would occur in three stages while the cuts to cereals prices would take place in two steps . Both of these changes were necessary in order to finance the increased expenditure in the beef sector. The financial question, however, remained unresolved; no agreement on a method for budget stabilization was reached. More problematic was that the compromise reached and approved by the Agricultural Council exceeded the spending limits the Economic and Financial Affairs Council proposed for the CAP by €7 billion. In the final agreement on this compromise within the Agricultural Council, Portugal was outvoted and France issued a reserve d’attente on the grounds that the financial problems had yet to be resolved . The lingering financial issue and France’s reserve d’attente facilitated the re-opening and further amending of this agreement by the European Council at the Berlin Summit. The Agricultural Council knew that although their agreement concluded negotiations for the CAP amongst the ministers of agriculture, further revision was still possible by their heads of state or government. They acknowledged as much in their formal press release to outline their compromise stating “the reform of the CAP is part of the Agenda 2000 package and that no part of this [agreement] can be considered definitively agreed until final agreement is reached on Agenda 2000 as a whole” . The European Council, which is comprised of the heads of state or government for all the member states, met at the Berlin Summit March 1999 to reach a final agreement on Agenda 2000. As part of these negotiations, the CAP deal reached by the agricultural ministers was re-opened by Jacques Chirac. As a former minister of agriculture who maintained close ties with the farming community, Chirac was considered an expert on the subject, and was arguably more knowledgeable on the agricultural policy and the inner workings of the CAP than any of his colleagues on the European Council, including Gerhard Schröder, Germany’s newly elected Chancellor from the left who chaired the Berlin summit. As it was a period of co-habitation in the French government, Chirac was particularly concerned with appeasing a core right constituency, the agricultural community, specifically those in the beef and cereals sectors . Fischler was aware that Chirac was willing to go to great lengths to cater to these interests. As one high level Commission official recounted, Chirac made Fischler well-aware of his displeasure with Fischler’s reforms to the beef sector when Chirac visited Fischler in the middle of the night during the negotiations and told him, , “I am the father of beef intervention and you are trying to destroy my scheme” .