Author: naturehydro

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

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

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

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

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

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Environmental pressures will further limit the possibility for land expansions

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

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

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

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

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

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Institutional barriers also constrain producers from moving into individual farming

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

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

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

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

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

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

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

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

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

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

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Climate factors that affect microbial diseases are multifarious and multiplicative

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

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

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

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

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

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It is produced primarily under aerobic conditions but also can be generated in the absence of oxygen

Global models that have incorporated both biophysical and socio-economic parameters have predicted that negative impacts on food production from climate change will largely be felt in the developing world, but positive impacts will be felt in the developed world . These studies conclude that the magnitude of this disparity will be determined by which future IPCC’s emissions scenario is adopted and the degree to which crops will respond to CO2 fertilization. Low latitude regions of the world may not benefit from CO2 fertilization, because the benefits are overshadowed by the predicted detrimental effects of increased temperature and/or precipitation changes . As a result, regions such as Africa or parts of Asia are predicted under the GHG intensive A1fi scenario to experience yield reductions up to 30% of 1990 levels by 2080 . The population at risk of hunger in Mali, for example, is predicted to increase from the current 34% to 44%, due to land degradation and then up to 72% due to the additional impacts of climate change by 2030 . These regions are at particular risk because their lack of infrastructure and technology impedes their producers’ ability to adapt to adverse and/or altered climate conditions. In contrast, the stress caused by A1fi climatic conditions is expected to be offset for some crops such as cereals , by the effects of CO2 fertilization, resulting in small increases in yield in Australia, North America, and South East South America.Assessments of agricultural production in the United States have used an Integrated Assessment approach, which includes complex interactions of temperature and precipitation changes with increased climate variation, changes in pesticide use, environmental effects caused by agriculture , changing global markets, societal responses, and technological adaptation, to model agricultural response to climate change . Consistent among these studies are the conclusions that there will be a dramatic difference in regional impacts,blueberry packaging box but agricultural production in the United States overall will increase, commodity prices will fall and irrigation use will go down due to increased precipitation and potentially higher water use efficiency that results from CO2 fertilization .

Climate change is therefore expected to be economically positive for U.S. consumers and negative for producers, but will entail increased pesticide use and result in increased environmental degradation . Regional-level forecasts could be quite different in California than nationally, due to its limited water resources and its focus on specialty crops.Water supply is central to the success of Californian agriculture. In addition to changes in precipitation, water availability will likely be influenced by rising temperatures, and consequential increases in water demand from other sectors . Increased temperatures will affect the amount of water collected and stored in the Sierra snow pack. By the end of the century, the Sierra snow pack is predicted to be 30% to 70% lower than the current winter total, due to an increase in rainfall vs. snowfall, and earlier melting of the snow pack . This will be most prominent in the southern Sierra Nevada, and at elevations below 3,000 m where 80% of California’s snow pack storage currently occurs. The changing availability of water both within California and to California agriculture, may lead to heavy reliance on groundwater resources, which are currently over drafted in many agricultural areas . Approximately 42% of current ground and surface water is used for agricultural purposes . Demand for water resources will be further exacerbated by an increase in the population of California in the coming century, which is projected to be > 46 million people by 2030, and may reach 90 million by 2100 . As will be discussed below, gradual shifts in climate over the next hundred years will necessitate adaptations that may not necessarily require direct government intervention, and could be driven, largely by market forces, changing management practices, and technological advances . California agricultural producers have had a history of adapting to new locations, development of water resources, and changes in markets. New adaptations will be made easier and more efficient by the availability of predictive information to producers, and an appropriate policy environment. Some sectors also lend themselves to more rapid change than others. For example, perennial tree crops and vines, of which many are unique to California in the U.S. context, may be particularly vulnerable to problems.

The adaptation to rapid change or extreme climatic events, such as floods, droughts, and heat waves are much more difficult to predict. Such extreme events may exceed the adaptive capacity of markets and be much more difficult for producers to cope with . Thus, development of risk and response strategies to various extreme climate change scenarios may gain more attention in the coming years. Beyond responding to changes in climate, California producers will most likely find opportunities to mitigate the release of GHG. Agriculture will play a significant role in a portfolio of national mitigation strategies, for example, as a first step to sequestering carbon . United States agriculture and forestry could remove more than 425 million metric tons of carbon equivalents of combined greenhouse gases , based on modeling of extreme increases in carbon prices. Carbon trading could have substantial impacts for agriculture, such as increased crop value and reduction of environmental externalities. Greenhouse gases include carbon dioxide , nitrous oxide , methane , and high global-warming-potential gases such as sulfur hexafluoride , hydrofluorocarbons , and chlorofluorocarbon . Since these gases absorb the terrestrial radiation leaving the earth’s surface, changes in the atmospheric concentrations of these gases can alter the balance of energy transfer between atmosphere, land, and oceans. All atmospheric GHG concentrations are increasing each year due to anthropogenic activity, which, in turn, leads to climate changes at the local, regional and global scale . The present section focuses in particular on CO2, N2O, and CH4 because they are the three major bio-genic GHGs produced by the agricultural sector in California and across the globe. It summarizes current sources and sinks of GHGs, i.e., total amounts of emissions by each type of gas, the contribution of the agricultural sector to California GHGs emissions, and consider agriculture and forests as potential sinks of GHGs. Potential impacts of climate change on CO2, N2O, and CH4 emissions and possible mitigation strategies for GHGs produced by the agricultural and other sectors are presented. California produced 493 million metric tons of CO2-equivalent GHGs emissions and in 2002 was ranked as the second largest U.S. state emitter after Texas . Most emissions were CO2 produced from the combustion of fossil fuels from industrial and transportation sources. Overall, the contribution of the agricultural sector to GHG emissions as a whole in California is relatively small. Taken together, agriculture and forestry contributed approximately 8% of the state’s total GHG emissions, including GHGs from all agriculturally related activities such as fossil fuel combustion associated with crop production, livestock production,blueberry packaging containers and soil liming . Emissions arising from transportation of agricultural commodities are not included in this estimate. CO2 emissions from non-fossil fuels, including agricultural activities, were 2.3% of the total GHG emissions of California. Of the 2.3% of CO2 from non-fossil fuels, agricultural activities contributed about 38%. Thus, the total contribution of CO2 from agricultural activities to the total GHG emissions was 0.9% in 2002 .

Nitrous oxide and CH4 emissions contributed 6.8% and 6.4%, respectively, to the total GHG emissions in 2002, with approximately 59% and 38%, respectively, originating from agricultural activities. An estimated 18.6 and 0.9 metric tons of CO2-equivalent GHG came from agricultural practices and manure management, respectively, in 2002 . Thus, the agricultural contribution to the state’s 2002 emissions of N2O and CH4 was 4% and 3%, respectively. Methane emissions from California flooded rice fields constituted a total of 0.5 metric tons of CO2-equivalent GHG and constituted less than 2% of total CH4 emission in California in 2002 . Methane emissions from animal production included 7.3 and 6.6 metric tons of CO2-equivalent from enteric fermentation and manure, respectively .Greenhouse gases are produced primarily by soil microorganisms carrying out oxidation-reduction reactions, including nitrification, denitrification, methanogenesis and organic matter decomposition . Because changes in temperature and precipitation alter the activity of soil microorganisms, GHG emissions from agriculture would likely be affected by climate change. This section considers, in general terms, the impacts of climate change on respiration and soil organic matter dynamics, as well as N2O and CH4 emissions. Carbon dioxide is the end product of respiration by soil biota .A potential impact of increased temperature is loss of carbon from the large reservoir of C contained in SOM in agricultural and forest soils. Although forests are currently a sink for CO2 , they might become a source of CO2 with temperature increases from global warming . This issue is critical, because SOM contains roughly two-thirds of the terrestrial C and two to three times as much C as atmospheric CO2 . Many researchers have investigated the effects of temperature on decomposition rates of SOM in mineral soils. Trumbore et al. reported that temperature is a major controller of turnover for a large component of SOM, as long as soil moisture is not a limiting factor. It is hypothesized that the decomposition of soil labile C is sensitive to temperature variation whereas resistant components are less sensitive. However, Fang et al. suggested that the temperature sensitivity for resistant SOM pools does not differ significantly from that of labile pools, and that both pools of SOM will therefore respond similarly to global warming. If this conclusion is correct, more C will be released than predicted by the HadCM3 model that assumes an insensitivity of resistant C pools to temperature. In contrast to observations that decomposition is enhanced by increases in temperature, Giardina and Ryan reported, based on analyses of data from locations across the world, that rates of SOM decomposition in mineral soils were not controlled by temperature limitation to microbial activity and that estimates made from short term studies may overestimate temperature sensitivity. Because moisture content in soils strongly affects the activities of soil microorganisms in direct, and perhaps more importantly in indirect ways , changes in precipitation patterns due to global warming may be one of the main impacts of climate change on SOM decomposition in mineral soils. Predictions of changes in precipitation are problematic, differing among climate models, thus making influences on SOM difficult to predict. Other factors influence SOM decomposition , some of which may be affected by climate change, must also be considered in projections of how SOM will behave. Temperature should not be viewed in isolation from other factors . Unfortunately, the magnitude and relative importance of these factors in governing SOM dynamics have received little attention in the literature.Nitrous oxide is produced primarily during denitrification, an anaerobic microbial process in soils or sediments, in which nitrate is used as an electron acceptor in the absence of oxygen . Though nitrification, the oxidation of ammonium to nitrate, also produces some N2O, this process is thought to be less important than denitrification . Agricultural activities—soil emissions from fertilizer use, residue burning and animal production—are responsible for an estimated 80% of anthropogenic emissions of N2O . Few studies have investigated N2O emissions in agroecosystems in California. In a comparison of organic and conventional managed tomato soils in the Central Valley, N2O emissions were found to be of short duration; followed addition of organic or mineral fertilizer in the organic and conventional systems, respectively; and occurred immediately after irrigation events . There are, however, no published extensive, systematic studies collecting field measurements of N2O over the growing season in different soil types of California to permit identification of relationships between fluxes, management practices, and environmental variables. Other studies outside of California have indicated that emissions of N2O are primarily controlled by soil moisture content, in particular the water-filled pore space , temperature , organic carbon availability , and concentration of mineral nitrogen . The latter factor is often optimal in agricultural soils for N2O fluxes because addition of synthetic N fertilizers and organic manures lead to elevated mineral N concentrations at least temporarily. In addition, California agricultural systems are frequently irrigated, leading to ideal moisture conditions for denitrification and potentially N2O fluxes .

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The estimated productivity gaps in GLW are an order of magnitude larger than our estimates

The shift out of agriculture and into other more “modern” sectors has long been viewed as central to economic development. This structural transformation was a focus of influential early scholarship with the issue even stretching back to Soviet debates over whether to “squeeze” farmer surplus to hasten industrialization . A more recent macroeconomic empirical literature has revived interest in these issues, often using data from national accounts . This body of work has documented several important patterns that help shed light on the sources of income differences across countries. First, it shows that the share of labor in the agricultural sector correlates strongly with levels of per capita income: most workers in the poorest countries work in agriculture while only a small share do in wealthy countries. Importantly, while income per worker is only moderately larger for non-agricultural workers in wealthy countries relative to poor countries, agricultural workers are many times more productive in rich countries. This creates a double disadvantage for poor countries: agricultural work tends to be far less productive in low-income countries, yet the workforce is concentrated in this sector.Studies that explore the closely related gap between the urban and rural sectors reach similar conclusions. Several recent studies have examined the extent to which these productivity gaps across sectors can reasonably be viewed as causal impacts rather than mainly reflecting worker selection. By a causal impact of sector,wholesale grow bags we mean that a given worker employed in the non-agricultural sector is more productive than the same worker employed in the agricultural sector. In contrast, worker selection would reflect differences driven by the fact that workers of varying ability and skill levels are concentrated in particular sectors.

This paper seeks to disentangle these two competing explanations by estimating sectoral wage gaps using unusually long-run individual-level panel data from two low-income countries, Indonesia and Kenya. If there are causal impacts of sector, the large share of the workforce employed in the agricultural sector in low-income countries could be viewed as a form of input misallocation along the lines of Hsieh and Klenow and Restuccia and Rogerson . The resolution of this econometric identification issue, namely, distinguishing causal effects from selection, is not solely of scholarly interest: the existence of causal sectoral productivity gaps would imply that the movement of population out of rural agricultural jobs and into other sectors could durably raise living standards in low-income countries, narrowing cross-country differences. The existence of large causal sectoral productivity gaps also raises questions about the nature of the frictions that limit individual movement into more productive employment, and the public policies that might promote such moves or hinder them . Gollin, Lagakos, and Waugh and Young are two important recent studies that explore this identification issue. GLW examine labor productivity gaps in nonagricultural employment versus agriculture using a combination of national accounts and repeated cross-sectional data from micro-surveys, and document a roughly three-fold average productivity gap across sectors. In their main contribution, GLW show that accounting for differences in hours worked and average worker schooling attainment across sectors—thus partially addressing worker selection— reduces the average estimated agricultural productivity gap by a third, from roughly 3 to 2. They also find that agricultural productivity gaps and per capita consumption gaps based on household data remain large but tend to be somewhat smaller than those estimated using national labor surveys, possibly in part due to differences in how each source measures economic activity. GLW remain agnostic regarding the causal interpretation of the large agricultural productivity gaps that they estimate. If individual schooling captures the most important dimensions of worker skill and thus largely addresses selection, GLW’s estimates would imply that the causal impact of moving workers from agriculture to the non-agricultural sector in low-income countries would be to roughly double productivity, a large effect.

Of course, to the extent that educational attainment alone fails to capture all aspects of individual human capital, controlling for it would not fully account for selection. Young examines the related question of urban-rural differences in consumption , rather than productivity, and similarly finds large cross-sectional gaps.Using Demographic and Health Surveys that have retrospective information on individual birth district, Young shows that rural-born individuals with more years of schooling than average in their sector are more likely to move to urban areas, while urban-born individuals with less schooling tend to move to rural areas. Young makes sense of this pattern through a model which assumes that there is more demand for skilled labor in urban areas, shows that this could generate two-way flows of the kind he documents, and argues that he can fully explain urban-rural consumption gaps once he accounts for sorting by education.3 The current study directly examines the issue of whether measured productivity gaps are causal or mainly driven by selection using long-term individual-level longitudinal data on worker productivity. Use of this data allows us to account for individual fixed effects, capturing all time invariant dimensions of worker heterogeneity, not just educational attainment . We focus on two country cases – Indonesia and Kenya – that have long-term panel micro data sets with relatively large sample sizes, rich measures of earnings in both the formal and informal sector, and high rates of respondent tracking over time. The datasets, the Indonesia Family Life Survey and Kenya Life Panel Survey , are described in greater detail below.4 For both countries, we start by characterizing the nature of selective migration between non-agricultural versus agricultural economic sectors, and between urban versus rural residence. Like Young , we show that individuals born in rural areas who attain more schooling are significantly more likely to migrate to urban areas and are also more likely to hold non-agricultural employment, while those born in urban areas with less schooling are more likely to move to rural areas and into agriculture.

We exploit the unusual richness of our data, in particular, the existence of measures of cognitive ability , to show that those of higher ability in both Indonesia and Kenya are far more likely to move into urban and non-agricultural sectors, even conditional on educational attainment. This is a strong indication that conditioning on completed schooling is insufficient to fully capture differences in average worker skill levels across sectors. We next estimate sectoral productivity differences, and show that treating the data as a repeated cross-section generates large estimated sectoral productivity gaps, echoing the results in existing work. In our main finding, we show that the inclusion of individual fixed effects reduces estimated sectoral productivity gaps by over 80 percent. This pattern is consistent with the bulk of the measured productivity gaps between sectors being driven by worker selection rather than causal impacts. Specifically, we first reproduce the differences documented by GLW for Indonesia and Kenya, presenting both the unconditional gaps as well as adjusted gaps that account for worker labor hours and education . These are large for both countries,grow bags for gardening with raw gaps of around 130 log points, implying roughly a doubling of productivity in the non-agricultural sector. When we treat our data as a series of repeated cross-sections, the gaps remain large, at 60 to 80 log points. These are somewhat smaller than GLW’s main estimates, though recall that GLW’s estimates using household survey data also tend to be smaller. Conditioning on individual demographic characteristics as well as hours worked and educational attainment narrows the gap, but it remains large at between 30 and 60 log points. Finally, including individual fixed effects reduces the agricultural productivity gap in wages to 4.7 log points in Indonesia and to 13.4 log points in Kenya, and neither effect is statistically significant. Analogous estimates show that productivity gaps between urban and rural areas are also reduced substantially, to zero in Indonesia and 13.2 log points in Kenya. We obtain similar results for the gap in per capita consumption levels across sectors where this is available for Indonesia. This is useful since consumption measures may better capture living standards in less developed economies than earnings measures, given widespread informal economic activity. Furthermore, we show that the productivity gap is not simply a short-run effect by demonstrating that gaps do not emerge even up to five years after an individual moves to an urban area. We also find that productivity gaps are no larger even when considering only moves to the largest cities in Indonesia and Kenya .

Our methodological approach is related to Hendricks and Schoellman , who use panel data on the earnings of international migrants to the United States, including on their home country earnings. Mirroring our main results, the inclusion of individual fixed effects in their case greatly reduces the return to international migration . Similarly, McKenzie et al. show that cross-sectional estimates of the returns to international immigration exceed those using individual panel data or those derived from a randomized lottery. Bryan et al. estimate positive gains in consumption in the sending households of individuals randomly induced to migrate within Bangladesh, although no significant gains in total earnings. Bazzi et al. argue that cross-sectional estimates of productivity differences across rural areas within Indonesia are likely to overstate estimates derived from panel data using movers. Other related studies on the nature of selective migration include Chiquiar and Hanson , Yang , Beegle et al. , Kleemans , and Rubalcava et al , among others. A limitation of the current study is that we focus on two countries, in contrast to the scores of countries in GLW and Young . This is due to the relative scarcity of long-run individual panel data sets in low-income countries that contain the rich measures necessary for our analysis. That said, the finding of broadly similar patterns in both countries, each with large populations in two different world regions, suggests some generalizability. Another important issue relates to the local nature of our estimates, namely, the fact that the fixed effects estimates are derived from movers, those with productivity observations in both the non-agricultural and agricultural sectors. It is possible that productivity gains could be different among non-movers, an issue we discuss in Section 2 below. There we argue that, to the extent that typical Roy model conditions hold and those with the largest net benefits are more likely to move, selection will most likely produce an upward bias, leading our estimates to be upper bounds on the true causal impact of moving between sectors. However, absent additional knowledge about the correlation between individual preferences, credit constraints, and unobserved productivity shocks, it is in principle possible that selection could bias our estimates downward instead. Similarly, it is possible that very long-run and even inter-generational “exposure” to a sector could persistently change individual productivity due to skill acquisition, and this opens up the possibility that selection and causal impacts are both important. We return to these important issues of interpretation in the conclusion, including ways to reconcile our estimates with existing empirical findings. The paper is organized as follows. Section 2 presents a conceptual framework for estimating sectoral productivity gaps, and relates it to the core econometric issue of disentangling causal impacts from worker selection. Section 3 describes the two datasets ; characterizes the distinctions between the non-agricultural and agricultural sectors, and urban vs. rural areas; and presents evidence on individual selection between sectors. Section 4 contains the main empirical results on productivity gaps, as well as the dispersion of labor productivity across individuals by sector, consumption gaps, dynamic effects up to five years after migration, and effects in big cities versus other urban areas. The final section presents alternative interpretations of the results, and concludes. We present a development accounting framework to disentangle explanations for the aggregate productivity gap across sectors. We consider both observable and unobservable components of human capital, and whether intrinsic worker preferences for sector may bias direct measurement of the productivity gap. A standard model suggests that worker selection is most likely to bias sectoral productivity gaps upward when estimated among those moving into non-agriculture but lead to a downward bias when estimated among those moving into agriculture.

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Exposures to pesticides in the third trimester did not increase risk for preterm birth

Each PUR record includes the name of the pesticide’s active ingredient, the poundage applied, the crop type, and the location and the date of application. The California Department of Water Resources performs countywide, large-scale surveys of land use and crop cover every 7–10 years. Land use maps increase spatial resolution because they provide more detailed land use geography that allows us to refine the pesticide applications . We then combined PUR records, land use maps, and geocoded birth addresses to produce estimates of pesticide exposure during pregnancy. Monthly exposure estimates were calculated by adding the poundage of pesticide applied in a 2-kilometer buffer surrounding each address and weighting the total poundage by the proportion of acreage treated within the buffer. Previous pesticide studies relied on different buffer sizes from 500m , half a mile , 1000m , 1250m , 1600m , 5000m , to up to 8000m distances , depending on the pesticide of interest, landscape, and weather conditions. In light of previous research, the buffer of 2-km we chose, will provide a reasonable distance for assessing pesticide applications around residential addresses. For each calendar month, our integrated GIS-system returned continuous measures for each specific chemical applied within 2-km of individuals’ residences. We defined the first, second, and third trimesters as 0-12 weeks, 13-25 weeks, and ≥26 weeks of pregnancy, respectively. For preterm birth, the length of gestation and hence exposure period are shorter than term birth by design; to account for that, we assessed the third trimester exposures using 27- 32 weeks of gestation only since more than 88% of all preterm births had a gestational length longer than 32 weeks. For each pesticide, daily poundage for each gestational day of pregnancy was calculated based on monthly values,seedling starter pot and then averaged across all days in each trimester. We then categorized prenatal exposure as ever/never exposed to a specific chemical in each trimester.

We selected 17 individual chemicals previously observed to have reproductive toxicity . Additionally, we also considered all pesticides from three widely used chemical classes that have been linked to reproductive toxicity based on the Pesticide Action Network pesticide database , i.e. 24 n-methyl carbamate/dithiocarbamates, 50 organophosphates, and 29 pyrethroid pesticides to which one or more study subjects were exposed according to our 2km buffer criterion . For each class, we used the sum of the total number of individual chemicals that each subject was ever exposed to in each time period of interest. We divided subjects into high , low , and no exposure to the respective pesticide, and compared high and low with the no exposure group as the reference. Since information about the specific location of non-agricultural pesticide applications are not provided by the PUR and because some individuals in urban areas are highly exposed to traffic-related air pollution or hazardous air toxics that are known risk factors for adverse birth outcomes , we restricted our analyses to individuals born in agricultural regions, defining those as residences within 2km buffer of any type of agricultural pesticide application during pregnancy . We conducted unconditional logistic regression analyses adjusting for matching factors and the source of control subjects and estimated odds ratios and 95% confidence intervals . To account for the unbalanced gender ratio and birth year distribution in this combined sample, we included the inverse of the sampling fraction as a stabilized weighting factor to reflect the sex and birth year distribution of all California births. Statistical analyses were performed using SAS software, Version 9.4 . We additionally adjusted for covariates as potential confounders and effect measure modifiers based on the literature : including maternal age at delivery , maternal race/ethnicity , maternal birthplace , maternal education , parity , payment source for prenatal care as a proxy for family income , prenatal care in the first trimester , and a previously developed neighborhood-level SES metric . Furthermore, we conducted stratified analyses by maternal race/ethnicity since exposures may be higher among Hispanics, especially recent immigrants, who may live close to agricultural fields and have poor housing conditions ; by infant sex because males are more likely to be born preterm ; as well as by season of conception , estimated from the last menstrual period and length of gestation, because of seasonal variations in pesticide applications .

In sensitivity analyses, we compared effect estimates with and without adjusting for two risk factors for adverse birth outcomes, maternal cigarette smoking during pregnancy and prepregnancy Body Mass Index , calculated as maternal pre-pregnancy weight divided by maternal height  for births in 2007-2010 only, since these variables are only available on the birth certificate from 2007 onward. We also investigated the potential confounding effects from outdoor air pollution that can impact fetal growth during critical periods among the autism controls only due to data availability. We estimated trimester-specific exposures to local, traffic-derived NOx, PM2.5, and CO, including roadways within 1.5 km of subjects’ birth addresses, i.e. inter-quartile range -scaled measure of NOx as a local traffic marker derived from the CAlifornia LINE source dispersion model model . Additionally, we adjusted for co-exposure to at least one of other individual chemicals as a single variable when assessing each individual chemical, and estimated mutually adjusted ORs for the three chemical class exposures during the same exposure window. When evaluating later trimester exposures we adjusted for exposure during prior pregnancy periods, because these effect estimates may be altered by earlier exposures . Since a low geocode quality is likely to introduce spatial exposure misclassification, we excluded those with a geocode quality at the USPS Zip Code Area centroid level or coarser. Lastly, we examined spontaneous vaginal deliveries only, excluding medically indicated preterm deliveries more likely to be due to severe maternal pregnancy complications including pre-eclampsia and gestational diabetes that might or might not be in the causal pathway for pesticide exposures and the outcome. Infants born preterm or born term with low birthweight were more likely to have mothers of younger age, less education, lower neighborhood SES, starting prenatal care after the first trimester, and using Medi-Cal or other government programs instead of private insurance. In addition, infants born preterm were more likely to be a third or later born child, and have mothers with Hispanic or Black race/ethnic background; infants born term but with low birthweight were more likely to be female and a first born child, and born to Black and Asian mothers . First- and second trimester exposures to some pesticides we have selected were associated with a small increase in risk for preterm birth. Specifically, in multivariate adjusted models, first trimester exposures to glyphosate compounds, paraquat dichloride, chlorpyrifos, imidacloprid, permethrin, dimethoate, and methyl bromide, and second trimester exposures to chlorothalonil, glyphosate compounds, paraquat dichloride, simazine, and imidacloprid, yielded adjusted ORs between 1.03 and 1.07 with 95% CIs excluding the null value .

Maternal education changed the OR estimates the most among all covariates.Effect estimates were generally slightly stronger in female infants, except for simazine, which shows stronger effect in males with an OR of 1.06~1.07 . Stratified analysis by season of conception suggested that effect estimates were generally stronger when the peak season of pesticide application concurred with the first or second trimester of pregnancy . When examining chemical classes,round nursery pots first trimester exposures to carbamates , or pyrethroids increased ORs for preterm birth in the high exposure group, compared with the no exposure group, while second trimester exposures to carbamates, organophosphates, or pyrethroids were all associated with small increases in ORs for preterm birth . We generally did not observe elevated ORs for preterm birth among male infants, but observed a stronger 7–11% increase with exposure during the first or second trimester among female infants . Exposure prevalence and effect estimates were generally stronger in infants born to the foreign born or US-born Hispanic mothers than White mothers . Associations between the selected individual pesticides or chemical classes and term low birthweight for each trimester in pregnancy were mostly null. In multivariate adjusted models, we only saw increased ORs for second or third-trimester exposures to myclobutanil ; similarly, exposures to the three chemical classes were not associated with term low birthweight in general, except for marginally elevated odds in infants exposed to 2 or more pyrethroids . Results were similar in our sensitivity analyses, with additional adjustment for maternal prepregnancy BMI and maternal smoking in the years 2007-2010, for NOx as traffic-related air pollution, or restricting to those with a high geocode quality only. For each individual pesticide, adjusting for co-exposure to other pesticides resulted in attenuation of odds by 2-3%; ORs mutually adjusted of three chemical classes or adjusted for prior exposures were mostly similar to or slightly decreased; the mutually adjusted OR for pyrethroids was most stable, suggesting a more robust association with pyrethroids, which were used more in recent years . ORs were generally stronger when we restricted to spontaneous preterm births only for both individual chemicals and chemical classes. In this large California study of women living within 2km distance from agricultural fields on which pesticides were applied, we found that early and mid-pregnancy exposure to selected pesticides known or suspected to be reproductive toxicants and chemicals in the classes of pyrethroids and possibly also carbamates or organophosphates, are associated with a small to moderate size increase in risk of preterm birth between 1998 and 2010.

We found little evidence for pesticides being related to term low birthweight, except for exposures to pyrethroids as a class further corroborating their adverse influence on pregnancy observed for preterm birth and possibly one single pesticide myclobutanil – however, this might have been a chance observation given that we tested 17 individual chemicals. Yet, term low birth weight is a much rarer event than preterm birth and we had less statistical power to estimate small effects accurately. Our positive findings for preterm birth are consistent with biomarker-based studies with measured organophosphates, or pyrethroids and their metabolic breakdown products in maternal blood or urine or umbilical cord blood , though most of the literature assessing environmental exposures to pesticide found inadequate evidence for associations with preterm birth . Less than a handful of studies conducted in the US examined associations for environmental exposures to pesticides from agricultural applications and preterm birth and/or low birthweight and provided month- or trimester-specific estimates . These studies were almost exclusively conducted using California’s unique PUR system, nevertheless they differed in terms of how they assessed exposures and pregnancy outcomes. Our study was in line with an earlier study in the San Joaquin Valley that assessed pesticides labeled with EPA signal word toxicity by summing up their active ingredients applied in the 2.6 km2 section surrounding maternal residences and reported high exposure to pesticides increased risks of preterm birth and low birthweight by 5-9% overall . In contrast, one study reported mostly negative associations between spontaneous preterm deliveries and exposure to 69 chemical groups or 543 specific chemicals in 1998-2011 in the San Joaquin Valley , perhaps because this study focused on late pregnancy instead of early or mid-pregnancy, which is believed to be the critical period for exposures causing preterm birth , and in addition a ‘live-birth selection bias’ could in part explain the negative effect. The other study in northern California reported methyl bromide use within 5 km of mother’s home was also positively associated with gestational age in the first trimester; yet their results were sensitive to buffer size and could potentially be confounded by chloropicrin or diazinon, often used conjunctively with methyl bromide . Maternal, placental, and fetal factors are thought to determine risk of preterm birth and may be affected by prenatal exposure to environmental chemicals . For example, it is known that chlorpyrifos can cross the placenta and enter the fetus, possibly altering the growth and development of the fetus . Mechanisms by which pesticides may affect risks of preterm birth include interference with immune pathways and inflammation , or with metabolic and endocrine regulatory pathways as well as oxidative stress . For example, in-vitro study results suggested that phosmet and chlorpyrifos alter cell viability and induce an inflammatory cytokine profile, indicating that organophosphates may adversely affect trophoblast cells .

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Climate models are widely used to study the effects of agriculture on climate

With respect to sociodemographic factors, mothers that are exposed to extreme levels of pesticide are more likely to be minorities and have lower education than the sample population as a whole. While we control for these factors, there is potential for the high exposure sample to differ in other unobserved ways that could yield a higher likelihood of adverse birth outcomes. If so, this would result in overestimates of the effects of pesticide exposure on adverse birth outcomes. Additionally, we measure pesticide exposure as all pesticide use on production agriculture in the 2.6 km2 PLS Section encompassing mothers’ addresses. We do so because the diversity of chemicals applied in the San Joaquin Valley is extensive and the cumulative effects of multiple exposures are not well understood. However, some chemicals or combinations of chemicals may not be relevant to reproductive risk. Thus, our coefficients are likely underestimates for individuals exposed to a disproportionately high fraction of chemicals of reproductive concern for their PLS Section, year and birth month. There is some indication that closer proximity to agricultural fields results in increased odds of adverse birth outcomes. For a study of this spatial and temporal breadth it is infeasible to directly measure distance from a sprayed field. However, for the San Joaquin Valley, PLS Sections that have any agriculture generally are agriculturally dominated. Furthermore, the PLS Section is roughly 2276 m on a diagonal. Thus it is highly likely that the vast majority of households in PLS Sections with pesticide use are within 1000 m of a treated agricultural field. If pesticides dissipate much more rapidly,cut flower bucket such that the effect is concentrated within 100 m of pesticide use, our study design would underestimate this relationship due to dilution with individuals living farther away from fields but still within the same PLS Section exposure.

However, for this to be occurring, the population residing on-farm or adjacent to fields must be much smaller than the broader population residing in the San Joaquin Valley for us to observe such small coefficients. Indeed, this makes intuitive sense for California, where farm workers overwhelmingly report living independent of their employers in houses or rental units, particularly if they have a spouse or children. However, our results may under predict adverse birth outcomes in regions where a larger proportion of workers reside in employer-provided housing on or adjacent to fields, where a larger fraction of pesticide are applied aerially, or where permissible chemicals are more environmentally persistent or toxic to humans. We also lack information on residence time at mother’s address and employment. Much of the San Joaquin Valley economy is driven by the agricultural industry. If farm workers were mostly migratory and followed the harvest, our measure of residential pesticide exposure would be inaccurate for this subset of the population. Yet, according to the National Agricultural Workers Surveys for 1996–2011, California farm workers, especially if they have a spouse or children in their household, are settled. Our measure of exposure would also be artificially high if women were applying agricultural pesticides during pregnancy. While ~18% of California farm workers are women, only 1.5% of women reported using pesticides in the past 12 months and 0% of women with a spouse or children had reported doing so. Women could get additional exposure via their spouses, and ~16% of male farm workers reported loading, mixing or applying pesticides in the past year. Finally, the San Joaquin Valley is well known to have substandard environmental quality, frequently exceeding EPA contamination levels for air quality. If such exposures co-vary with pesticide use and vary at small spatial and temporal scales, the coefficient on pesticide exposure could capture additional contamination despite our PLS Township-year and birth month controls. While we cannot be certain we have eliminated all sources of contamination that co-vary with pesticides, including a rich set of ambient air quality and temperature metrics did not change the basic results of this paper. In conclusion, there is a growing literature on the relationship between pesticide exposure and adverse birth outcomes. Yet, evidence of a causal link between infant health and agricultural pesticide exposure remains uncertain due to small samples and lack of maternal or birth characteristics.

Our study is the most comprehensive to date, bringing together the largest data file ever compiled on street-address level birth outcomes and fine scale exposure to agricultural pesticides. We provide robust evidence that there are multiple negative effects of residential agricultural pesticide exposure on adverse birth outcomes, but only for births exposed to very high levels of pesticides during gestation. The documented concentration of impacts in the extreme upper tail of the pesticide exposure distribution may explain why previous studies fail to consistently detect effects of pesticides on birth outcomes. Furthermore, the concentration of impacts in the extreme tail of the pesticide exposure distribution provides policy challenges and public health opportunities to balance these potentially serious but rare outcomes with the societal benefits of continued pesticide use.Although the response of agricultural systems to climate is drawing considerable attention because of the potential for a global food crisis, current understanding of how climate affect agricultural production is highly uncertain since the feed backs between them are not well studied. Agricultural systems are highly vulnerable to climate variability, where the area suitable for agriculture, the length of growing seasons and yield potentials are expected to change under warming scenarios [IPCC, 2007]. In addition, crop growth alters some important physical climate forcings, such as latent heat flux, shortwave radiation, long wave radiation and soil moisture. This two-way interaction is often referred to as a feedback, describing a nonlinear cycle between two systems. Clarifying the importance of these feed backs could improve regional climate simulations in agriculturally intensive areas and enable better prediction of crop production. Variability in atmospheric CO2, temperature and precipitation highly affect agricultural production. The elevated CO2 could increase photosynthetic productivity [Aoki and Yabuki, 1977; Cooper and Brun, 1967; Moss, 1962] and therefore lead to an increase of yield. Amthor [2003] reviewed the previous observations and suggested doubling CO2 could increase the yield by 31% in average. At the same time, double CO2 could lead to 34% reduction of transpiration and double water use efficiency [Kimball and Idso, 1983]. In one study, increase in variability of temperature and precipitation resulted in significant increases in yield variability and crop failures [Mearns et al., 1992]. Warming by 2-4 o C could results in substantial shortening of the growing season, and change of crop calendar, particularly in winter [Butterfield and Morison, 1992]. Furthermore, increasing temperature and precipitation could have different impacts on yields for different crops. For example, a simulation study indicated potato production was increasing while wheat and faba bean was decreasing with increased temperature, and increasing of precipitation had no effect on the yield of potatoes or spring wheat, but could reducing winter wheat yield [Peiris et al., 1996]. Meanwhile, agriculture also affects climate by altering the surface energy, water, and carbon cycle. Cropland plays a very important biogeophysical role in changing climate [Feddema et al., 2005; Foley et al., 2005; Pitman et al., 1999].

Agricultural expansion in business as usual scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans [Feddema et al., 2005]. Crops alter the small-scale boundary layer structure [Adegoke et al., 2007], such as surface wind and boundary layer height, by increasing canopy height during the growth process. Compared to natural vegetation,flower display buckets cropland has higher albedo that alters the energy budget when converting between forest and cropland [Bonan, 2008; Oleson et al., 2004]. Cropland also alters the water cycle. Both field observations and modeling have shown that conversion of forest to cropland can reduce evaportranspiration and precipitation at the regional scale [Sampaio et al., 2007]. Moreover, agriculture and associated management practices were found to affect the carbon cycle [Lal, 2004]. Global simulation indicates a 24% reduction in global vegetation carbon due to agriculture [Bondeau et al., 2007a]. Growing biofuel crops at previously natural vegetation land could increase greenhouse gas emissions by 50% [Searchinger et al., 2008]. Both observations and numerical modeling are used to study climate effects on agriculture. Laboratory studies using growth chambers and greenhouses showed elevated CO2 could increase net photosynthesis [Aoki and Yabuki, 1977; Cooper and Brun, 1967; Moss, 1962]. These stuides had a short period measurements and the high CO2 concentrations were not realistic. Free air CO2 enrichment experiments [Ainsworth and Long, 2005; Ainsworth et al., 2002; Long et al., 2006] using long term observation confirmed some chamber experiment results that trees were more responsive than herbaceous species to elevated CO2, but crop grain yields increased far less than in previous enclosed studies. Regression models [Rosenberg, 1982] also have been employed to study how climate affects crop yield and this method is still widely used today [Diffenbaugh et al., 2012; Lobell et al., 2008b]. Finally, crop growth models enable yield prediction and hazard prevention.Climate models were first developed for numerical weather prediction in the 1950s, and had a very coarse resolution only contained atmosphere circulation. In 1960-1970s, the climate model included both ocean and atmosphere circulations. In 1980-2000s, the development of regional climate model and sub-grid physical process model not only aim to improve the forecasting skill but also to study the climate change. In climate model, the land surface model provides sensible, latent, and momentum flux for atmosphere model to solve the atmospheric equations. The potential climate sensitivity to land use change is determined by the difference between two simulations that differ only in land use. A key determinant in accuracy of such research is how well the land surface model simulates the surface energy fluxes . The development of land surface model is getting more and more comprehensive to reflect the reality [Bonan, 2008]. Early land surface models represented the physical processes using simple parameterizations. For example, the soil hydrology was represented as a bucket, which could hold some maximum amount of water filled by precipitation, with the excess water becoming runoff.

Currently, most land surface models include all the major parameterizations, such as vegetation photosynthesis and conductance, snow accumulation and melting, radiation transfer, and turbulence processes above and within the canopy, etc. Moreover, some advanced land surface models include the carbon cycle and dynamic vegetation growth. Coupling a land surface model that incorporates dynamic crop growth into a climate model enables simulation of the two-way interactions between climate and crop growth. Recent work incorporating crop growth models into climate models has revealed that dynamic crop growth strongly influences regional climate patterns by altering land surface water and energy exchange. Most of these studies have not rigorously evaluated results against observations of climate and crop variables. Further, interactions between crop growth and irrigation effects on climate are not well examined. The aim of the work is to improve a regional climate model by incorporating a land surface model that simulates dynamic crop growth. Particularly, my work focuses on the improvement and evaluation of the Weather Research and Forecasting Model with updated Community Land Model , a dynamic crop growth model, and an irrigation scheme. As the next-generation mesoscale numerical model, the standard version of WRF includes relatively simple land surface schemes, which potentially constrain model applications for studying the land surface and ecosystem-atmosphere feed backs. By adding the CLM into WRF, I expected an improvement in surface energy flux simulations. Therefore, I first validated the performance for the surface energy fluxes for four vegetation types across the continental of United States in the first chapter [Lu and Kueppers, 2012]. Since one problem in this model was related to the low crop LAI bias and lack of irrigation, I further incorporated the dynamic crop growth model and irrigation into a new version . I evaluated the crop growth and climate variables in the new version and the influence of dynamic crop growth on irrigation effects was quantified. In the third chapter, I used the coupled model to study irrigation effects on heat wave frequency, duration, and intensity.

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The cost of operation can sometimes be offset through the production of commercial crops

Further measures in 1988, including drainage of evaporation ponds as well as covering of the deeper ponds with fill soil, led to the elimination of aquatic habitat at the Kesterson Reservoir, thus preventing any additional waterfowl from being exposed to selenium at the location . Despite the closure of the Kesterson Reservoir, problems of excessive selenium concentrations persisted in the greater surrounding Grassland wetland area , the largest freshwater wetland ecosystem in California . Farmers of the Grassland drainage area had historically discharged their surface and subsurface runoff through the natural channels of the Grassland wetlands to the San Joaquin River . As a result of increased scrutiny following the events at Kesterson , 33 km2 of the Grasslands were added to California’s Clean Water Act section 303 list of impaired waters due to excessive selenium concentrations in 1988. The wetland’s two major flow channels Salt and Mud Slough followed in 1990 . In 1996, the Grassland Bypass Project was created to amend this situation . The GBP consists of a series of measures to reduce selenium loads in the Grassland marshes and the San Joaquin River, including the reopening of a stretch of the San Luis Drain bypassing the wetlands . The GBP is analyzed below in the section entitled “Selenium load reduction coupled to conveyance into the San Joaquin River”.Two fundamental approaches have been used to manage seleniferous runoff in the San Joaquin Valley: local disposal and conveyance out of the San Joaquin Valley. Geographically,procona buckets the separation between these two approaches aligns with the drainage areas defined under the San Joaquin Valley Drainage Program’s grand management plan . The southern subareas Kern, Tulare and, after the San Luis drain closure, also the Westlands subarea dispose of runoff locally, while the Northern and Grassland subareas convey drainage to the San Joaquin River .

The two approaches share common elements . In either case, methods are employed to decrease the disposal load by first decreasing drainage production and then decreasing the volume and selenium concentrations of the drainage itself . Fundamentally, only the final disposal step differs, with drainage either being evaporated and the salts disposed, or channeled to be diluted in a larger water body .The debate on how to sustainably manage selenium loads and drainage needs in the Central Valley is far from over. Mass balance analysis by the USGS reveals that the drainage needs of the Westlands subarea, the greatest near surface selenium reservoir among the subareas, cannot be met without the retirement of at least one third of the 1,200 km2 of agricultural lands and the use of treatment methods for selenium and salt removal that are as of yet unproven . For a detailed discussion of proposed management scenarios for this subarea the reader is referred to the Final Environmental Impact Statement of the San Luis Drainage Feature Re-evaluation and the technical analysis of proposed plans by Presser and Schwartzman . The current plans under discussion for the Westlands in terms of selenium removal include reverse osmosis and reductive precipitation in microbial bioreactors that have so far only been tested at the pilot scale . It is uncertain whether the proposed bioreactors will be effective with the high salinity inputs expected to result from reverse osmosis . Given such uncertainty I focus on the proven management methods by which seleniferous drainage is actively being managed in the San Joaquin Valley .Locally, seleniferous runoff can be treated using technologies that physically, chemically or biologically remove selenium from water, reused through irrigation of designated land planted with salt tolerant crops, or disposed of in evaporation facilities. A number of removal technologies have been proposed post-Kesterson , however the physical and chemical methods are too costly and the biological methods fail to reduce selenium concentrations in treated drainage to below 2 µg/L at relevant scales . Thus, for lack of better alternatives, drainage reuse and evaporation ponds are so far the local remediation and disposal methods of choice. The reuse of seleniferous drainage as irrigation water on designated reuse plots thus reducing the water volume requiring final disposal, has seen a marked expansion over the last decade, with areas of reuse in the Grassland subarea alone increasing from 7.4 km2 to more than 20 km2 from 1996 to 2009 .

A number of innovative approaches often involving a sequence of increasingly salt tolerant crops in either time or space have been developed and tested in the San Joaquin Valley . Of the 2,600 kg selenium produced in the Grassland subarea in 2009, about half were disposed through drainage reuse . The exact fate of this selenium is as of yet unclear. In particular, there are concerns about the long term sustainability of drainage reuse, after increasing concentrations of dissolved selenium were observed at all monitored soil depths in the only study monitoring soil selenium for an extended time period on a reuse plot in the San Joaquin Valley . Additionally, there are concerns that endangered wildlife such as the San Joaquin kit fox , the kangaroo rat , and the blunt-nosed leopard lizard may be adversely affected if their ranges overlap with reuse areas . Evaporation ponds, which are shallow basins used for the evaporative disposal of drainage water, are deployed primarily in the Tulare and Kern subareas . Since there are no drainage channels or rivers to convey drainage out of these two subareas, evaporation is the only option for final disposal . The design of evaporation ponds has been optimized post-Kesterson to reduce wildlife use and thus the risk of exposure to elevated selenium concentrations in pond water . Specifically, steep levee slopes, elimination of windbreaks, and a minimum water depth of 0.6 m are used to deter waterfowl. Enhanced solar evaporator designs that use sprinklers to ideally eliminate standing water altogether have been tested at the pilot scale, but have not seen wide-spread deployment to date . Evaporation ponds are currently exempt from water quality guidelines that apply to natural waters, however management needs to include active and continuous measures to limit wildlife use through hazing and removal of pond vegetation. Additionally, the provision of nearby alternative habitat for waterfowl is recommended when selenium concentrations exceed 2 µg/L . While these management procedures are necessary during the operation of evaporation ponds to avoid ecological damage, they also greatly increase the cost of operation. In fact, the owners of many of the privately operated evaporation ponds in the San Joaquin Valley have decided to cease operation as a result of imposed requirements .

Another drawback of this method of local disposal is that the evaporate needs to be stored at dedicated disposal sites. This is particularly problematic if evaporation is chosen to continuously dispose drainage from large source areas. For example, it has been estimated that if the drainage program in the Westlands district is expanded as planned, up to 400,000 tons of salt may need to be disposed yearly,procona florida containers which would require dedicated dumpsites covering an area of around 1 km2 every 50 years .The Northern and the Grassland subareas channel a large portion of their runoff to the San Joaquin River. Among the two, only the Grassland subarea has been marked by problematic selenium loads . Due to the Grassland Bypass Project , it is also the subarea in which management of seleniferous runoff has made the greatest progress over the last 20 years . The GBP was created in 1996 through an agreement between the U.S. Bureau of Reclamation and the regional drainage entity of the Grassland Area Farmers under the legal umbrella of the San Luis and Delta-Mendota Water Authority. It consists of three central measures. First, a 45 km stretch of the San Luis drain was opened to convey subsurface drainage from the GAF to Mud Slough, thereby bypassing most of the wetlands . Second, limits were imposed on the total allowable selenium discharge by the GAF and these limits were set to decrease over time . Finally, ongoing monitoring of water quality and quantity was initiated across the project area to enforce limits and gage impact . The original 5 year project was extended by 8 years in 2001 and then by 10 years in 2009 . Whereas the use agreement specified load dependent incentive fees for exceedance of the specified selenium load limits, the true innovation consisted in enabling the GAF to develop an internal selenium load trading program . This trading program represents the first ever cap-and-trade style program for any pollutant allowing trades directly between non-point sources. It is also the first instance in which “total maximum daily load” requirements under the Clean Water Act have been successfully enforced against a non-point source . Since 2001 the drainage management efforts of the GAF have shifted away from the load trading program and towards centralized management for the region , however the overall strategy has remained consistent . Funds obtained as part of the GBP are used to support programs and actions aimed at reducing selenium loads , such as the local drainage reuse measures described above. The direct aid provided by the GBP as well as the economic incentives for load reduction and conservation initially created by its load trading program have led to an overall reduction in selenium loads .

In fact, the total annual loads of selenium discharged through the San Luis Drain have decreased continuously over the course of the project, from 3,110 kg in 1997 to 560 kg in 2009. This has been reflected in a reduction of selenium loads in the San Joaquin River from a pre-project annual average of 3,690 kg to 690 kg in 2009 . Weekly selenium concentration monitoring data collected below the confluence with the Merced between 1996 and 2012, reveal seasonality in selenium loads , but reaffirm the overall load reductions reported by the GBP . However, the reduction is primarily due to a reduction in total discharge from 6.1×107 to 1.6×107 m3 in 2009. Average selenium concentrations of discharge decreased from 67 to 33 µg/L in the same time period and were still significantly above the water quality criterion of 5 µg/L. Much of the reduction in selenium loads can be credited directly to the impact of the projects and measures implemented as part of the GBP. For example, through the San Joaquin River Water Quality Improvement Project, more than 20 km2 of land have been purchased and planted with salt tolerant crops providing the capacity to reuse up to 1.9×107 m3 of drainage water per year . A GBP unrelated factor in the successful load reduction was the prevalence of drought during the early 1990s which incentivized investments in efficient irrigation technology and other conservation measures on the side of the GAF . Therefore, considerations of best management practices and technology aside, the creation of quantitative economic incentives for selenium load reduction should be a priority of any seleniferous drainage management program. Unfortunately, comprehensive incentive programs such as the GAF load trading program remain the exception rather than the rule, for selenium as they do for many other pollutants.The primary motivation of the GBP was the protection of the sensitive wetland habitat in the Grassland area. Through circumvention, the GBP has effectively removed all seleniferous drainage water from 145 km of channels that supply water to more than 650 km2 of sensitive wetlands and wildlife areas . This led to a rapid decrease in selenium concentrations at the monitoring stations in this area after the GBP’s implementation in 1996. In Salt Slough for example , concentrations have dropped from 16 µg/L in water year 1996 to below 2 µg/L for most of the project duration . In addition, selenium concentrations monitored in the tissue of various fish species collected at the slough , minnows , and others) have dropped from toxic levels between 1992 and 1995 to below levels of concern after 1998 . Accordingly, the slough was removed from California’s 303 list of impaired waters in 2008 .