Tag: greenhouse vertical farming

While each of the 34 organizations identifies farmland protection as an important objective, the degree of emphasis often varies. The names of land trusts and open space districts suggest this variation; the terms “agriculture,” “farmland” or “rangeland” are found in only 11 names, sometimes in combination with “open space” or other designations. Based on our review of mission statements and interview comments, we sorted the 34 organizations into three categories, according to differing agricultural emphases . Only a third of the 34 organizations are unwavering in their exclusive or primary focus on farmland. We included some trusts that focus on grazing cropland properties and others that are primarily interested in cropland. The California Rangeland Trust and Amador Land Trust concentrate on foothill ranch land, while the Monterey, South Livermore Valley and Yolo trusts focus almost entirely on orchards, vineyards, vegetable growing parcels and other croplands. In recent years, some organizations with broad or multiple conservation objectives have begun to emphasize farmland protection, perhaps motivated by new funding opportunities for farmland easements created by state government and other agencies. Some land trusts have also reassessed their conservation objectives to reflect community concern about farmland loss and increased landowner interest in easements. Two such programs are the Napa County Land Trust and the Mid-Peninsula Regional Open Space District , neither of which emphasized farmland protection in their original missions. Following a planning exercise conducted by its board, the Napa trust in 1999 established agricultural lands as its top conservation priority, partly to support the agricultural preserve created for the Napa Valley floor by county government policy. As a result of an advisory voter referendum and the encouragement of other conservation groups,ebb and flow trays in 2000 the Mid-Peninsula district extended its boundaries to an area of prime farmland along the San Mateo coast.Interviewees frequently commented on the connection between protecting agricultural activity and preserving natural resources such as habitat, wetlands and scenic views.

The same easements, some said, could accommodate both purposes. Others, however, discussed serious limitations, citing conflicts between cultivation and other aspects of commercial agricultural production, and the preservation of natural resources. At its most general level, the argument for compatibility simply views farmland as additional open space, a landscape free from human congestion and an antidote to urbanization.“If you are not working with ranchers and farmers, you are not going to get any open space,” noted the manager of a Bay Area program. Emphasizing the open space values of agricultural easements appeals to urban residents and helps build community wide support. “We realized that in order to appeal to more people, we have to recognize that agricultural land is also open space under private ownership,” the Bay Area manager said. It is a step further to focus on the compatibility of agricultural activity with specific plant and animal resources and landscape features . According to some managers of programs that concentrate on ranch land, cattle grazing has beneficial effects on local habitats. They note that controlled grazing helps to cut back nonnative grasses and reduce the possibility of wildfires. One land trust manager who works with ranchers said: “Our primary objective is to provide alternative ways to address the economic viability of rangeland agriculture and to conserve the natural balance of the ecosystem. We see the two as being intertwined. And so we try to provide services and education to ranchers about how they can integrate their economic needs with the environmental and ecological needs of their rangeland. I think there’s been a shift in the way the cattle industry looks at these issues. Many people are beginning to see that they’ve got assets on their ranch that are not necessarily related to the commodity that they produce, whether that’s open space, or recreational opportunities or watershed values” . There is far less compatibility for farm operations that involve intensive cultivation and chemical applications, including orchards, vineyards and vegetables and parcels devoted to confined animal production . While the potential for protecting natural resource lands is ever present in the easement priorities of organizations that focus on cropland, this clearly takes a back seat to their emphasis on protecting commercial agriculture.

The Yolo Land Trust makes a sharp distinction between two types of easements: “A farmland conservation easement contains restrictions to keep the land in agriculture. A habitat conservation easement is written to protect the habitat value of the land” . By these standards, intensive farming conflicts with efforts to preserve highly sensitive habitat, such as vernal pools, other wetlands and riparian corridors — conditions that also restrict cattle grazing in particular areas. A related but separate issue is the possibility of opening easement-protected properties to public access. Most farmers interested in selling an easement explicitly reject such use, citing liability problems and interference with farm operations. This severely limits the use of easement-protected farmland for trails and other recreational purposes, highly desired open space amenities for urban populations. Despite these incompatibilities, program managers we interviewed identified a number of examples of easements created primarily for the protection of agricultural operations, including crop production, that also serve habitat preservation purposes. Some cover sizable parcels that allow for the geographic separation of the different uses. For example, the Mendocino Land Trust acquired a 430-acre easement with 60% devoted to agriculture and 40% in preserved oak woodlands. Several easements held by the Yolo Land Trust are used mainly for crop production but are traversed by streams with riparian corridors closed to cultivation. Some interviewees suggested that easements are not the best option for preserving sensitive habitat and providing public recreation, because of the complications generated by private ownership. The better approach, instead, would be outright purchase and ownership by public or nonprofit agencies, simplifying management and perhaps allowing low-intensity agricultural operations on a lease basis. One land trust manager noted that government agencies and foundations that fund environmental easements usually prefer to support fee purchases, especially in areas removed from urban pressures where easement prices per acre tend to be relatively low. Indeed, several organizations in our study with significant non-agricultural goals both hold easements and own and manage large parcels as nature preserves or recreational sites.Thirty-four local conservation organizations in California seek to protect farmland via the acquisition of conservation easements. About a third focus exclusively or primarily on farmland, while the greater number fit this objective into broader conservation agendas that include the preservation of lands with natural resource values. The degree to which programs seek individual easements to achieve both farmland and resource protection varies.

The objectives are compatible or incompatible, depending on the agricultural commodities that are grown, cultivation practices and the natural resources to be protected. The smaller number of programs expressly focused on farmland, especially those concerned with protecting cropland, tend to make a sharp distinction between different conservation purposes, but on occasion they also recognize secondary resource values in some of their agricultural easements. Concerned primarily with identifying California’s agricultural conservation programs and their missions, we did not thoroughly examine issues of compatibility. A broader research approach is needed to for this purpose, one that examines in detail agricultural practices and impacts in different environmental settings and the application of sustainable agricultural techniques.Agriculture’s role in the process of economic growth has framed a central question in development economics for several decades . While arguments differ regarding the specific mechanisms through which agricultural productivity increases might contribute to structural change in the economy, it has long been theorized that advances in the agricultural sector can promote shifts in labor to higher productivity sectors that offer higher real incomes. Empirical work in more recent years has helped inform the conceptual arguments and underscored the long-term growth and poverty reduction benefits from agriculture, especially for the most extreme forms of poverty . At the same time, recent evidence has also underscored the role of the manufacturing sector in driving structural change and long-term convergence in incomes across countries . This and other evidence regarding agriculture’s relatively low value added per worker compared to other sectors has prompted some researchers to narrow the number of developing countries in which agriculture is recommended as a priority sector for investment in light of higher prospective growth returns in non-agricultural sectors . These debates present a first-order concern for understanding why some countries have not experienced long-term economic progress and what to do about it. If agriculture can play a central and somewhat predictable role within the poorest countries,ebb flow tray then it is a natural candidate for targeted public investment. The theoretical and empirical literature regarding structural change is vast, yet identifying the causal role of agricultural productivity is challenging because relevant indicators of structural change trend together in the process of development; impacts on labor force structure are likely to occur after a lag; and statistical identification is not amenable to micro-style experiments. Our contribution in this paper is to focus on the role of agricultural inputs as drivers of higher yields and subsequent economic transformation, using the unique economic geography offertilizer production in our identification strategy. Large-scale nitrogen fertilizer production occurs in a limited number of countries around the world, owing partly to the fact that the Haber-Bosch process requires natural gas. Transporting this fertilizer to each country’s agricultural heartland generates cross sectional variation due to economic geography, akin to Redding and Venables’ model of “supplier access” to intermediate goods, which is estimated to affect income per capita. Our identification strategy exploits this variation in supplier access as well as temporal variation in the global fertilizer price to generate a novel instrument for fertilizer use. To our knowledge this is the first application of economic geography towards causally identifying the relationship between agriculture and structural change. Our paper builds on the insights of Lagakos and Waugh , which highlight the gaps in understanding of cross-country variations in agricultural productivity. A variety of studies have estimated sources of total factor productivity in agriculture in the poorest countries, including in sub-Saharan Africa , but agriculture is such an input-intensive sector that TFP assessments only provide one piece of the overarching crop sector puzzle. Our econometric strategy proceeds in two parts. First, we empirically assess the inputs that contributed to increased productivity in staple agriculture, as proxied by cereal yields per hectare, during the latter decades of the 20th century.

Using cross-country panel data, this forms a macro-level physical production function for yield increases. We find evidence for fertilizer, modern variety seeds and water as key inputs to yield growth, controlling for other factors such as human capital and land-labor ratios. Second, we deploy our novel instrument to examine the causal link between changes in cereal yields and aggregate economic outcomes, including gross domestic product per capita, labor share in agriculture, and non-agricultural value added per worker. We find evidence that increases in cereal yields have both direct and indirect positive effects on economy-wide outcomes. The results are particularly pertinent when considering economic growth prospects for countries where a majority of the labor force still works in agriculture. The next section of this paper motivates the empirical work, drawing from the many contributions in the literature towards understanding structural change. Section 3 presents empirical models both for estimating the physical production function for cereal yields and for estimating the effect of yield increases on economic growth, labor share in agriculture, and non-agricultural value added per worker. Section 4 describes the data, Section 5 presents the results, and Section 6 concludes. At the most general level, agricultural output can grow through either increases in area planted or increases in output per area planted . In the agronomic science community, primary emphasis is placed on the latter, with land productivity usually measured in tons of output per hectare. The term “green revolution” is typically used to describe the early stage where yields jump from roughly 1 ton per hectare to 2 or more tons per hectare. The term was coined following the advent of South Asia’s rapid increases in cereal yields in the late 1960s and 1970s. Some researchers have argued that these green revolutions underpinned later stages of economic growth, and cite Africa’s lack of a green revolution as a key reason why the region has not yet experienced greater long-term economic success .

The agriculture-based countries have, by definition, a high share of total poverty in the rural sector and a high share of GDP growth originating in agriculture, the latter fundamentally because agriculture accounts for a large share of GDP. They include all the SS-Africa countries else than South Africa as a transforming country and some mineral-rich countries. The current role of agriculture in the development of these countries is not only for industrialization and for economic diversification away from agriculture . Instead, in the emerging paradigm, agriculture has multiple functions for the development of these countries in helping trigger growth at early stages, reduce poverty, increase food security, equalize gender status, reduce ruralurban income disparities, conserve resources, and provide environmental services. These multiple functions can be win-win, but more generally imply trade-offs and the consequent need for country-level priority setting in deciding how to effectively use agriculture for development.Agricultural growth in SS-Africa has been lagging relative to other regions of the world, especially in value added per capita. The latter has stopped declining since 1994 but remains sluggish in a comparative perspective . Area expansion has been the main source of output growth in cereal production in SS-Africa, by contrast to East and South Asia where rising yields were the main source of growth, and to Latin America where area expansion that was initially the main source of growth has also given way to rising yields . However, more than in most other parts of the world, rising land scarcity has become a stark reality for SS-Africa, compromising reliance on area expansion as a future source of growth,rolling benches and calling for emulation of the Latin American growth reversal . Yet, cereal yields have been overall stagnant while those in other regions have increased steadily , with some better performers such as South Africa, Côte d’Ivoire, and Zambia showing the way forward.

Accelerated growth thus requires, and will increasingly require, gains in land productivity that have to this date not materialized at an aggregate level, i.e., a “Green Revolution” for Africa.The food crisis with higher and more volatile international market prices is particularly threatening to SS-Africa where most countries are net food importers and where most of the population spends a high share of its income on food staples.The transmission from international to domestic prices has been highly uneven across countries and commodities, ranging from high transmission for rice in Senegal, Cameroon, and Ghana to low transmission in most other countries, in particular Madagascar, Guinea, Niger, and Malawi, with Uganda an intermediate case . Higher transmission for a particular commodity tends to be associated with greater import dependency and lower diversification in consumption, but the determinants of transmission are also highly idiosyncratic to countries depending on policy interventions,real exchange movements, transactions costs on markets, and the competitive structure in imports and processing. Frequently assumed full transmission has led to overblown predictions of impact. There is a price policy dilemma originating in the contrast between: more stable consumer prices for imported foods with eventual shocks as in the 2007-08 food crisis where there is high transmission as in Senegal, and prices disconnected from the international market for local cereals but with very high and visibly rising variability . The food situation for the world, and especially for SS-Africa, has changed drastically in the last five years. New pressures have emerged both on the demand side associated with continued rapid population growth, income effects, demand for bio-fuels, and on the supply side originating in fluctuating energy prices, climate change, water scarcity, soil depletion, and pandemic zoonotic diseases.

With rising international market prices signaling that supply is overall not keeping up with demand, and rising price volatility in a context of low international grain stocks, defensive trade policies, and speculative movements on commodity markets, much greater focus needs to be placed on the supply side of food, and in particular on achieving sustainable productivity gains and greater resilience to shocks. Dealing with international market price volatility and domestic yield instability raises anew the issue of food security as a major policy concern, when it had slipped off the policy agenda following structural adjustment and trade liberalization. This requires revising policy decisions regarding trade when international market prices for staple foods are more volatile, use of national food reserves, social safety nets for the vulnerable, supply response in agriculture for greater domestic self-sufficiency, and promotion of subsistence farming for “farm-financed social welfare” for those beyond the reach of social safety nets when exposed to shocks. Securing access to food thus requires focusing not only on chronic poverty but also on vulnerability to transitory poverty, with a need to adjust current social assistance programs that are better equipped to deal with the former than with the latter. Assessment of the welfare incidence of price changes needs careful identification of net sellers and net buyers among rural households, most often revealing the surprising fact that a large majority of landed households are in fact net buyers of food, and thus negatively affected by higher and more volatile prices.In spite of greater public concern with agriculture created by the food crisis, the resilience of rural poverty, and the contributions of agriculture to climate change, public budgets allocated to agriculture in Sub-Saharan Africa still fall short of the 10% NEPAD guideline and of the 15% allocated to agriculture by successful Asian countries . Similarly, overseas development assistance allocated to agriculture in SS-Africa remains at historically low levels with only modest improvements after 2006 . There is also continued under-investment in agricultural research when comparing rates of return on investment to the opportunity cost of capital shows that there are high payoffs from such investments. The neglect of agriculture is also apparent in structural adjustment programs that have been highly detrimental to the institutional infrastructure of agriculture, followed by highly incomplete reconstruction of an alternative institutional structure. This includes market facilities, financial services, property rights, producer organizations, and governance for agriculture. An improved performance of agriculture clearly depends on greater attention to the institutions that serve the sector.

While reversing the neglect of agriculture requires increased public expenditures and overseas development assistance to agriculture, the financial crisis is likely to make commitments to agriculture by governments and international donors more difficult to be met. Greater emphasis must consequently be directed at improving the quality of public expenditures and of foreign assistance to agriculture, an area with considerable room for improvement. There are also encouraging new initiatives in progress including the CAADP policy guidelines, a more pro-active role for GFAR, increased budgets for the CGIAR, successful disbursements under the World Bank’s Global Food Crisis Response Program, and the Gates-Rockefeller Foundations’ Agra program. These initiatives need to be supported by high quality monitoring and impact evaluations for guidance and improvement, a support still largely incipient.One of the reasons for poor past performance of public investments in agriculture has been insufficient recognition of the difficulty in doing so. There should be no illusion that successfully using agriculture for development is a complex and multi-pronged enterprise that requires conceptualization, resources, capacity, coordination, political commitment, and time. Short run impacts on poverty are easier and faster to achieve via transfers , explaining the rising popularity of transfers over rising autonomous incomes as instruments, but they cannot be sufficient and adequate to solve the rural poverty problem. Rising autonomous incomes for the rural population has to be the main focus of sustainable poverty reduction strategies. Rainfed agriculture, that accounts for 88% of SS-Africa’s cultivated area, is characterized by a high degree of heterogeneity of conditions . Managing this heterogeneity requires decentralization and participation in order to design and implement local solutions. Heterogeneity also originates in highly varied social systems with a great diversity of institutional arrangements. Multiple constraints require a multi-sectoral approach. Key issues to be addressed include exhausted soils, insufficient infrastructure , low levels of education and health, a private sector limited by an uninviting investment climate, incipient producer organizations, and weak governance for agriculture. Small countries and large economies of scale in such investments as R&D and infrastructure invite regional cooperation. In using agriculture for development, the process through which growth in agriculture is obtained is as important as the outcome, in particular to achieve poverty reduction, gender equality, and environmental sustainability. Smallholder farming must for this reason be the dominant approach in spite of some advantages associated with large scale farming and the contracting out of land to international agribusiness that has recently been advocated by some development economists and pursued by some governments.We see these challenges being successfully addressed in large number of locations. The list of success stories is long and varied . It ranges from land certification schemes that provide security of access and support land rentals, to technological innovations in dealing with drought resistance and improved nutrition, ebb and flow bench more complete financial services that combine credit with savings and insurance, commodity exchanges to improve domestic market performance and create links with international commodity markets, extension systems that more effectively cater to clienteles in using IT capacities, community-driven development schemes with local participation to the delivery of public goods, and producer organizations able to not only serve their membership but acquire voice in the definition of public policy. These success stories need to be better identified, understood, and scaled up so they become reflected in aggregate statistics. Opportunities exist for profitable investments in agriculture, as can be seen in impressive gains achieved in the production of non-traditional exports.

And there is a clear renewal of interest in the private sector for the investment opportunities offered by agriculture. We cannot assume that we know the answers as to how to use agriculture for development in SS-Africa since a productivity revolution has to be idiosyncratic and locally adapted. Useful lessons can be derived from historical successes in other regions and from widespread local achievements in SS-Africa. There is a huge deficit of good social science scholarship applied to issues of agriculture for development in helping identify answers.The Special Feature on Diversified Farming Systems is motivated by a desire to understand how agriculture designed according to whole-systems, agroecological principles can contribute to creating a more sustainable, socially just, and secure global food system. “How to feed the world” is an increasingly urgent and looming concern voiced by many people, from local community groups to national and international governing bodies. By 2050, the world population is projected to rise to 9+ billion and food demands to double from current levels. At the same time, climate change, interacting with increasingly uneven access to declining oil, water, and phosphorus supplies, will greatly exacerbate the year-to-year unpredictability of agricultural production, potentially undermining the entire agricultural enterprise . Meanwhile, industrialized agricultural techniques are exacting a huge toll on surrounding environments, polluting waterways, creating dead zones in the oceans, destroying bio-diverse habitats, releasing toxins into food chains, endangering public health via disease outbreaks and pesticide exposures, and contributing to climate warming . Moreover, industrial agricultural methods are inherently unsustainable in mining soils and aquifers far more quickly than they can be replenished, and in their high use of fossil fuels . These numerous environmental and social externalities create a huge economic cost that industrialized food producers seldom pay. For instance, pesticide use alone causes up to $10 billion in damage to humans and ecosystems in the United States every year . Finally, although the agricultural sector currently produces more than enough calories to feed humanity, one billion people remain hungry and an additional one billion have micro-nutrient deficiencies . This paradoxical situation occurs because many people still lack access to sufficiently diverse and healthy food, or the means to produce it, which is primarily a problem of distribution rather than production . As further evidence of this paradox, global obesity rates have more than doubled since 1980 , reflecting an overproduction of food in industrialized countries that creates strong incentives for agrifood companies to absorb excess food production into processed foods and to market and distribute them to customers in supersized portions . This series of articles examines the proposition that diversified farming systems, with their focus on local production, local and agroecological knowledge, and whole systems approaches reduce negative environmental externalities and decrease social costs associated with industrialized monocultures, enhance the sustainability and resilience of agriculture, and contribute significantly to global food security and health.

These models are characterized by a complex set of linear inequality constraints that represent the production possibilities available to a farmer. The simplex optimization algorithm is used to select the optimum production possibilities. One disadvantage of this approach is that the solutions are restricted to extreme points in the multidimensional decision variable space and thus it is unable to explore intermediate solutions. A major problem with linear programming models is that they need complex constraint structures to achieve some degree of calibration to base data; those constraint structures restrict alternative solutions and are difficult to implement for applications such as adoption and impact of new technologies.Econometric methods have been developed and used for single crop production function models as well as single-equation and simultaneous system models that represent input demand and output supply behavior. Early work focused on primal representations and statistical estimation , but many efforts shifted to dual representations in the 1970s and later . Both static and dynamic models have been developed. Single crop production functions are estimated directly from data on the physical quantities of inputs and outputs observed from experimental plots, or, in later stages, from comprehensive farm production surveys. Heady was an early proponent and researcher in this area. In many cases the functional form for the production functions is a quadratic or Cobb Douglas specification, both of which have implicit restrictive assumptions on the production technology. Econometric estimation of agricultural systems was expanded to represent both multi-crop production with its associated interdependencies, the endogenous nature of agricultural supply response, and the imputed value of some key agricultural inputs that are often incompletely priced. A landmark article in this literature noted that multi-crop farm businesses responded to changes in prices or technology by adjusting both the intensity of input use per acre,vertical grow system the intensive margin ; and also the allocation of land to crops, the extensive margin. This distinction is important for modeling optimal input allocation in multi-crop farming systems.

The importance of the interaction of multi-crops in a farm unit was a significant step forward in realistic economic models of farming systems. However, the approach did not include formal linkage to biophysical models of agricultural processes. The econometric approach has limitations in its ability to extrapolate responses that are outside the estimation sample, or those that employ systems that are not present in the data sample. These limitations were emphasized by Antle and Capalbo in their development of economic simulation models that combine econometric and other disciplinary simulation models into an integrated assessment framework.The importance of risk on farm decisions was recognized early in the development of linear optimization models of farming systems. Early articles on this linear approach to risk analysis are by Lin et al. and Hazell and Scandizzo . As improved algorithms to solve quadratic optimization problems were developed, specification of risk expanded to a mean-variance measure of risk and imputed a risk aversion value based on observed farmer actions or primary surveys . Just and Pope introduced a widely-used econometric risk model. Antle introduced a general moment-based representation of output distributions that has been widely used to study production risk behavior, including downside risk. Recent research has extended this approach to investigate impacts of climate change .The importance of space in agricultural production and modeling agricultural systems was first introduced in terms of trade between regions of different comparative advantages. Takayama and Judge showed that spatial equilibrium conditions and transport cost between different production locations could be characterized as a quadratic optimization problem. Spatial econometrics advanced to include rates of development and specialization of production . Only recently has the availability of remotely sensed measures of agricultural land and water use led to the use of spatial econometrics methods to address spatially varying farm production . Techniques are emerging that use both remotely sensed data and spatial econometrics to draw conclusions about resource use or the effect of spatial variation on agricultural supply response.Complex simulation models have been used for the past 45 years to describe dynamic agricultural systems.

Early examples were often based on Forrester’s concept of system dynamics that uses storage and flow variables to describe the system. However the underlying philosophy that a comprehensive and complex feedback system is stable and reproducible has never been convincingly demonstrated. Structural simulation models can be useful for representing a combination of consistent behavioral relationships based on theory and empirical measurement. They are however, subject to interpretation in the absence of robustly estimated relationships describing system behavior. Various micro-economic models have been developed to simulate the economic behavior of agricultural systems and link behavior to environmental processes and economic sustainability indicators. van Wijk et al. document the large number and diversity of such models, that include: applications of various types of linear or non-linear programming models; household models ; agent-based models that incorporate spatial and temporal interactions among households; and models that link economic models with bio-physical crop, livestock, and environmental models. Recently, agent-based modeling has been widely used as a way of modeling interactive human behavior and natural systems. Some agent-based models have a more formal dynamic and calibration structure and use mixed-integer optimization approaches for solutions. However, the generality of the approach makes it susceptible to the same difficulties of empirical verification and reproducibility that earlier complex structural simulation models had. The population-based modeling approach of Antle et al. is a more parsimonious, generic approach designed to represent agricultural system heterogeneity. It links economic simulation models to bio-physical models to evaluate impacts of technology, policy and environmental changes on sustainability.Along with more complex constrained models, researchers have developed optimization models that utilize shadow values of resources and calibration constraints to derive nonlinear calibrating functions, which are termed positive mathematical programming . In the past 10 years PMP has developed from formal calibration methods that reproduce the observed cropping pattern to those that calibrate crop supplies to prior estimates of supply elasticities , and more complex production functions that calibrate against elasticities of substitution and returns to scale. In addition, PMP models are now being formally linked with biophysical models .

These macroeconomic models spawned a series of smaller-scale models which are usually called village or household models. General equilibrium village models account for all flows in the village economy and remittances within the village to different workers and landowners. In addition, they include flows of revenue in and out of the village boundary. This is particularly useful in developing country farm economies where much of the labor is supplied by family members with little or no pay. Another advantage of village-level equilibrium models is that they account for the utility gained from subsistence food grown in a village. These CGE models are anchored by a social accounting matrix that accounts for flows within and outside the economy. Moreover, it is common practice to fit the standard functional form such as a constant elasticity of substitution production, supply, or transformation function that is calibrated against exogenously estimated elasticities . CGE models have the disadvantage of being data and computationally intensive due to their more general specification, and for the quite restrictive assumptions required for their solution. Compared with more detailed partial equilibrium models, general equilibrium models are harder to incorporate detailed process models.Flichman describes recent studies on application of models that combine bio-physical and economic models to represent agricultural systems. Flichman and Allen and van Wijk et al. also survey economic agricultural system models. They characterize bio-economic models into farm, landscape, regional, and national models. Systems in each of these scales include crops, livestock, and socioeconomics components that interact in complex ways. For example, Fig. 5 shows components that need to be included in system models at the farm scale. These components and processes encompass the crop and livestock production enterprises of a farm, the household decision and production processes, and the interactions among the household and production systems of the farm. Within these scales the cited authors address both static and dynamic specifications. In his introduction Flichman attributes growth of bio-economic modeling to two developments: improvement of biophysical agricultural simulation models, and evolution of agricultural policies that demand integrated assessments that conventional economic models cannot provide. We briefly address three prominent areas of application of integrated bio-economic models.An alternative econometric approach to measuring the impact of climate change both on agricultural crop yields and on economic variables such as land values and economic returns is to estimate statistical models based on observed behavior. These statistical models are then simulated with data from future climate projections. A justification for this approach is that it can embed realistic adaptive behavior into the model . However, this type of model also has significant weaknesses. For example, it does not incorporate effects of CO2 fertilization on crop productivity,mobile grow system cannot represent changes in socio-economic conditions, and cannot be used to identify technological adaptations distinct from climate impacts. Various researchers have used statistical econometric methods to model the effects of climate on yields and other variables .Havlik et al. provides an overview of integrated livestock modeling and its use in mitigating climate change. Their analysis is driven by a large-scale economic optimization model that assesses crop bio-energy production, land-use changes, water requirements, and greenhouse gas emissions. Their results show that improvements in livestock production systems can significantly reduce impacts on fragile land use and improve the effectiveness of climate mitigation policies. In another approach, Kobayashi et al. analyzed stocking density impacts on Kazakhstan’s extensive rangelands using a stochastic dynamic programming model for multiple livestock systems with stochastic forage production. They showed that cost of capital strongly affects herd size and productivity.There is a rich history of modeling watershed and environmental quality, however much of this has not incorporated goals related to agricultural systems and only a few efforts incorporate crop and livestock models. There are at least two different perspectives about modeling across space, including the interconnectedness of agricultural and ecological systems across the landscape. The first perspective is that human systems, including the farm, communities, and administrative and political areas in which agricultural systems interact through decisions and policies, affect production systems, markets, and trade.

The other perspective is that the interconnectedness among hydrological and biophysical processes establishes the underlying behavior of agricultural systems over the landscape. This perspective leads to an emphasis on understanding physical, chemical, and biological processes that occur in watersheds. Both perspectives are important, yet agricultural models rarely consider both in the same assessments or models. There are many applications of watershed hydrology models, in particular using the SWAT model as discussed byGassman et al. , mainly focusing on environmental quality and water resource issues. Fig. 6 shows the regional integrated assessment approach developed by AgMIP that emphasizes linkages of agricultural systems across space using the first perspective noted above . In this perspective, based in part on the impact assessment approach developed by Antle , the focus is on the economic, environmental, and social impacts of alternative systems within heterogeneous household populations. However, this framework also illustrates the feed backs from farms to agro-ecological regions to national and global scales. We often use the term “scaling up” of model results to refer to the aggregation of model results from finer spatial resolutions to a larger area. If the areas of interest are defined by hydrologists, they tend to be watersheds. In contrast, if the areas are defined by economists, they tend to be administrative and political units or socio-economic stratifications . These perspectives are not mutually exclusive, however. In fact, they lend themselves to include both human and biophysical/hydrological processes. A challenge for next generation agricultural models is to include the technical aspects of integrated modeling and a transdisciplinary approach in which scientists recognize the need for collaboration, not only on specific projects, but also in designing models and decision support tools to achieve their goals. Many current agricultural system models have been developed to evaluate practices and policies associated with environmental quality. Biophysical models typically operate at the point/field scales with an emphasis on vertical fluxes of energy, water, C, N and nutrients throughout the atmosphere, plant, and soil root-zone continuum. Upscaling from point to the landscape scale requires estimation of surface and subsurface fluxes and ecological transitions along the lateral scale.

Food production, defined as the total yield of crops measured in tons, started at 3.4 million tons in 2000 for both scenarios, decreased to 1.9 million tons in 2009, and then increased to 2.9 million tons in 2015. For the future projections, from 2016 to 2020, the optimized scenario delivered more food than the baseline given the available land, but the baseline scenario showed steadily higher amounts of food production from 2022 to 2040 due to the fixed acreages and available water in that scenario. Food production decreased 8.5% under the optimization scenario.Groundwater has been over drafted in Pajaro Valley for decades . The modeling results from this study illustrate aquifer depletion, where 49 years of historical annual time series of inflows and outflows are estimated through a GBM and compared to PVHM . GBM results show an estimated annual average overdraft of −14.8 million m3=year from 1966 to 2009, close to the PVHM result of −15.9 million m3=year. Comparing the simulation models of GBM and PVHM has its limitations. GBM does not include certain inflows [landward underflow and stream flow infiltration, subtotal 17,930 thousands acre-ft ð TAFÞ=year on average] and outflows [storage flow depletion, storage depletion masked by seawater intrusion, outflows to the bay and tile drains, subtotal 18,910 TAF=year on average], which approximately cancel each other out. While these components contribute to the hydrology of the basin, GBM omits these components for simplification, and it represents a limitation of the model. The GBM was evaluated from 2016 to 2040 under two scenarios to estimate the average net groundwater storage of the area . Both scenarios began with an overall depletion of −57 million m3=year in 2015. This significant depletion of groundwater is related to the most recent multiyear drought in California that began in 2012,ebb flow tray which exacerbated the exploitation of groundwater resources because of the lack of groundwater regulations and policies in California before the passage of SGMA.

Both projections display similar behavior; however, the optimized scenario shows overall less groundwater depletion for the Pajaro Valley groundwater basin. The optimized scenario projects 10 years where the aquifer storage is zero or positive from 2020 to 2033. Both projections show the greatest depletion in 2040, with aquifer storage levels of −83 million m3=year and −48 million m3=year for the baseline and optimized scenarios, respectively.The highest storage point is 2 million m3 in 2033 and 35 million m3 in 2023 on the baseline and optimization scenarios, respectively. Overall, the GBM projections from 2016 to 2040 showed an average net groundwater depletion of −48 million m3=year for the baseline scenario in contrast with −10 million m3=year for the optimized scenario. This illustrates the possibility of a 79% increase in net groundwater storage from 2016 to 2040. Focusing on a shorter period from 2016 to 2030, the difference between projections is even greater with −47 million m3=year for the baseline scenario and −5 million m3=year for the optimization scenario, which is an 89% increase in net groundwater storage from 2016 to 2030. A single factor ANOVA evaluation indicated a significant difference in the net groundwater storage between scenarios. These findings support part of the study goal, which was that constraining agricultural water use can result in less groundwater overdraft. These results illustrate that groundwater simulation models can estimate future trends in groundwater depletion, consistent with previous studies in other agricultural and groundwater-dependent areas of California, while also validating the innovate application of optimization models to explore ecological and sustainable solutions to groundwater and land management challenges .

There are opportunities to improve water management in Pajaro Valley to reduce aquifer depletion and prioritize a reliable supply of freshwater for population demands and agriculture activities if farmers are incentivized to making collective decisions to optimize profits while managing groundwater sustainably.Successful productivity growth in agriculture has been the source of early development and subsequent structural transformation and industrialization in most of today’s high income countries. This has been amply documented by the work of historians such as Bairoch who analyzed at the Western industrialization experience, cascading from England in the mid- 1700s, to France and Germany around 1820, the United States and Russia in the mid-1800s, and finally Japan with restauration of the Meiji emperors in 1880. Following WWII, agriculture has similarly been the engine of growth and transformation for the Asian industrialization miracles in Taiwan, South Korea, China , and Vietnam . In all these countries, an agricultural revolution preceded a subsequent industrial take-off, typically by something like a half century. Agriculture has also fulfilled an important role in facilitating industrialization in countries like India, Brazil, and Chile . Agriculture remains today the expected engine of growth for the “agriculture-based countries”, those countries with a high contribution of agriculture to GDP growth and a high share of their poor in the rural sector . These are also countries where the farm population is importantly composed of smallholder farmers , in some cases exclusively and in others coexisting with larger commercial farms . In both cases, agricultural growth importantly requires modernization of the operation of SHFs. With labor-intensive industrialization increasingly compromised by robotization and the reshoring of industries toward the industrialized countries , agriculture with agroindustry and the associated linkages to services and non-tradable consumption has been heralded as a potentially effective strategy for GDP growth in these countries . This includes most of the Sub-Saharan Africa countries. This approach is a major departure from the classical structural transformation approach based on labor-intensive industrialization advocated in the dual economy models . The World Development Report Agriculture for Development’s main message was that agriculture-based countries should invest more in agriculture in order to fully capture its potential for growth and poverty reduction.

Following the world food crisis of 2008, there was a short-term positive response by governments, international organizations, and the donor community with a sharp increase in investment in agriculture. The number of countries meeting the CAADEP goal of allocating at least 10% of government expenditures to agriculture increased from 3 in 2007 to 10 in 2009. Overseas development assistance to agriculture increased by 60% between 2007 and 2009. But this response has not been sustained. In 2014 only 2 SSA countries out of 43 met the CAADEP goal. The modal SSA country spends only 5% of its public expenditures on agriculture. No SSA country spends a percentage of its public budget on agriculture that reaches the percentage contribution of agriculture to GDP, and 75% of the countries spend less than half that percentage . CAADEP also set a goal for public spending on agricultural Research-and-Development to reach 1% of agricultural GDP. Returns to investing in agricultural research are typically significantly in excess of cost relative to other public programs, indicating under-investment . This takes extreme forms in SSA where investment is by far the lowest among regions and has been declining over the last decade. In 2011, only six countries met the CAADEP research goal . With failure to invest in agriculture, the yield gap on cereals has continued to increase between SSA and other regions of the world. This gap is correlated with a growing chemical fertilizer gap and a large deficit in irrigation. Today, the World Development Report’s main message continues to be advocated by international development organizations such as the World Bank, the Food and Agriculture Organization, and the International Fund for Agricultural Development . This is motivated by the observation that 51% of the world extreme poor live in SSA, a share that continues to rise, and 78% of the world extreme poor work in agriculture in spite of rapid urbanization. Success in using Agriculture for Development is thus essential to meet the Sustainable Development Goals on poverty and hunger. In the current global economic context for the SSA countries,flood and drain tray investing in agriculture where the poor work has proven more effective for poverty reduction than taking the poor out of agriculture and to an urban-industrial environment through a Lewis -type structural transformation. Research shows that the poor are not found in agriculture due to adverse selection. Poverty reduction, where it has happened, has been more effective through productivity growth where the poor work than through structural transformation . A Solow-type decomposition of sources of growth shows that agricultural output growth in SSA in the 1985- 2012 period originated for 63% from area expansion compared to 8% from factor deepening and 29% from productivity growth . This is not sustainable due to an effective land constraint and declining farm size in most countries as a consequence of rapid population growth. Take Malawi as an example where agricultural land for households engaged in agricultural production fell from 2.3 acres in 2004, to 1.8 in 2010, and 1.4 in 2016 . Productivity growth and factor deepening consequently have to be the main sources of growth in SSA agriculture as in the rest of the developing world where they account for 83% of agricultural output growth.

This opinion on the role of agriculture for development is far from universally shared in the development community. Gollin et al. and Collier and Dercon have argued that rural poverty reduction has to come from employment creation in the urban-industrial environment and a structural transformation of the economy. As seen above, governments have correspondingly not invested public resources in agriculture to the recommended levels. Hence the puzzle in using agriculture for development is: why has the World Development Report/CAADEP recommendation not been followed? We argue here that it is because the mainly supply-side approach used for implementation has proved insufficiently effective, and needs to be complemented for this by a more explicit demand-side approach. There are African countries to look at for successful progress toward productivity growth in staple foods and a rural transformation . Chemical fertilizer use is overall low , but uneven across countries. The LSMS-ISA data show that the share of cultivating households using chemical fertilizer reaches 77% in Malawi, 56% in Ethiopia, and 41% in Nigeria, while remaining at 17% in Niger and Tanzania, and 3% in Uganda . Rural transformation is accompanied by land concentration in medium farms in countries like Kenya , Ghana , Tanzania , and Zambia . These farms are typically mechanized and owned by well-educated urban-based professionals who can be effective agents for technology adoption. These various success stories show that using agriculture for development can be done, but has not yet been sufficient to overcome aggregate rising gaps in yields between SSA and the rest of the world.While there has been limited success with raising public expenditures on agriculture, there has been considerable progress with data collection and with rigorous experimentation on how to promote the modernization of agriculture. We consequently know a lot more today about how to use agriculture for development than we did ten years ago, even though this knowledge has most often not been put into practice in the desirable form and to the desirable degree. It is consequently important to start by reviewing what we have learned. The main argument that has been used in support of the need for a structural transformation as the mechanism to grow and reduce poverty is that there is a large labor productivity gap between agriculture and non-agriculture . An important observation, however, based on the LSMS-ISA data for SSA is that while the gap in labor productivity per person per year between non-agriculture and agriculture is indeed large, the gap in labor productivity per hour worked is relatively small . In other words, when agricultural workers do work, their labor productivity is not very different from that of non-agricultural workers. What this suggests is that there is a deficit in work opportunities for agricultural vs. non-agricultural workers that creates an income gap between the two categories of workers. Because households engage in a multiplicity of sectoral activities, the relevant contrast in labor productivity is not between agriculture and non-agriculture, but between rural and urban households, with rural households typically principally engaged in agriculture. Looking at labor calendars for rural and urban households in Malawi in Figure 1, we see that weekly household hours worked are not different for rural and urban households at peak labor time, which corresponds to the planting season in December and January .

Despite the gains in market performance in recent years, WTO makes demands on China’s domestic agricultural markets. Domestic marketing policy response to the nation’s impending WTO accession has been substantial and will continue. Major changes are aimed at improving the efficiency of domestic market performance and minimizing the adverse shocks that may arise from external trade liberalization. Perhaps more than in any other sector, the reforms in cotton and grain markets that China agreed to in the final stages of China’s WTO negotiation clearly show that its leaders are using this opportunity to develop its health domestic agricultural market. The case of cotton presents a good example. In 1999, officials began experimenting in the North China Plain with marketing reforms for cotton, frequently considered the second most important strategic agricultural commodity . The reforms were aimed at improving cotton market performance by reducing market transaction costs, creating a market-oriented pricing mechanism, and integrating regional markets. The main policy measure sought to eliminate the current monopolized state-own cotton procurement and distribution system. In part reflecting the fact that informal markets had already been working for many years, the disruption to cotton markets after liberalization were almost non-existent. With a successful performance of this experimental reform, the liberalization policy was expanded substantially in 2000 and fully implemented in 2001. Domestically, over the past two decades state-owned grain traders have chronically performed poorly due to imperfect incentives and a number of taxing policy burdens. Although many companies have received considerable marketing subsidies,ebb flow bench the losses of these firms have always been a burden on the national leaders.

Moreover, although it had appeared reformers had solved this problem in the mid-1990s, retrenchments in agricultural policy created a situation in which many state-owned grain companies were still losing money in the late 1990s. Internationally, there were also concerns over several commonly executed policies that are now being addressed. For example, WTO negotiators expressed their opinion that China’s traditional ways of pricing agriculture were distorting. Others believe that the rights of state owned grain trading enterprises to procure commodities from farmers under special access rights give certain domestic firms unfair access and violates national treatment principles of WTO. Facing these pressures and concerns, China has launched a new set of reforms in the area of state grain marketing system in 2000. Building on past efforts to liberalize markets and continuing with the tradition of moving gradually, many believe that the measures included in this round are ultimately expected to have a defining influence on China’s grain markets in the years to come. As part of the first step, the restrictions on grain procurement for lower quality grains such as the early indica rice and maize in southern China, spring wheat in northern China, and all wheat in southern China were phased out in 2000. After the policy was set in place, any trader was allowed to buy or sell grain from any farmer or other trader at any time. Almost immediately this policy resulted in an adjustment in the structure of the cropping patterns in some regions. In the last several years, producers have begun planting varieties to improve grain quality. Many of these improvement will mean that to some extent China’s farmers are in a more competitive position to produce and sell varieties that might otherwise come in from foreign sources, for example, high quality rice from Thailand or high quality wheat from Canada or the US. With successful performance of grain “varietal” reform in 2000, leaders are now going to officially liberalize grain markets. Depending on the deregulation of all grain-related procurement and sales, leaders are first implementing their policies in a subset of grain-deficit, coastal provinces, Zhejiang, Jiangsu, Shanghai, Fujian, Guangdong, and Hainan. Currently, the government is planning to extend the implementation of the policies in all grain deficit provinces in 2002.

As seen in the past, given such close ties between traders in surplus and deficit areas, such policies in deficit areas will almost certainly naturally envelope all of China. In response to WTO accession, the government also have ambitious plans to increase investment in market infrastructure. Leaders see a need to establish an effective national marketing information network. Officials in the ministry of agriculture are attempting to standardize agricultural product quality and promote farm marketing. Some also have advocated the creation of agricultural technology associations. More generally, all of these moves are part of an effort by leaders to shift fiscal resources that used to be used to support China’s expensive price subsidization schemes to productivity-enhancing investments and marketing infrastructure improvements. The magnitude of this policy response is highlighted by the fact that the total subsidies for price and market interventions reached 40.3 billion in 2000, about 4 percent of national fiscal budget, more than any components of government expenditure in agricultural and rural area of bureaucrats that manage China’s agricultural policies, 93 billion on integrated agricultural development program, and 12.3 billion on poverty alleviation–CNSB, 2001. While not all of these funds were being used on distorting policies or non-productive administration, much of it was. If a good part of the funds is able to be redirected into more productive areas, there is a chance that the agricultural sector can be energized by this new windfall. The policy implications of China’s WTO accession on land use and farm organization also are hotly debated. Many of the concerns have arisen over the ability of China’s small farms to be able to compete after trade liberalization. Although every farm household in China is endowed with land, the average farm size is small, and declining . Leaders are pleased with the equity effects of the nation’s distribution of land as it allays concerns about food security and poverty. Land fragmentation and the extremely small scale of farms, however, almost certainly will in some way constrain the growth of labor productivity on the farm and hold back farm income.

The debate has centered on these issues: Some argue that farm size could be expanded and agricultural productivity could rise if policy makers were to advocate more secure land tenure arrangements. Others call for a continuation of policies that allow localities to periodically re-allocated land to the farmers in order to keep land in the hands of all rural residents. Although most policy makers currently seem to favor more secure rights, they still are searching for complementary measures that will not forego all of the pro-equity benefits of the current land management regime. Land ownership in rural areas, by law, is collectively owned by the village or small group and contracted to households . One of the most important changes in recent years has been that the duration of the use contract was extended from 15 to 30 years. By 2000, about 98% of villages had amended their contract with farmers to reflect the longer set of use rights . Although some were concerned that household and village demographics and other policy pressures often induce local authorities to reallocate land prior to contract expiration, it has been shown that the area of this reallocated land has been minimal and the effect on investment behavior insignificant . With the issue of use rights, resolved, the government is now searching for a mechanism that permits the remaining full-time farmers to gain access additional cultivated land and increase their income and competitiveness. One of the main efforts revolves around the development of a new Rural Land Contract Law. The Standing Committee of the National People’s Congress has drafted a law and the main body is expected to approve it in the near future. According to this law, although the property rights over the ownership of the land remains with the collective, the Law conveys almost all other rights to the contract holder that they would have under a private property system. In particular, the Law clarifies the rights for transfer and exchange of the contracted land, an element that may already be taking effect as researchers are finding increasing more land in China is rented in and out . The new legislation also allows farmers to use contracted land for collateral to secure commercial loan. Part of the law also allows family members to inherit the land during the contracted period. The goal of this new set of policies is to encourage farmers to use their land to increase their farms and household short and long-run productivity. Although quite controversial,4x8ft rolling benches the effort to increase China’s agricultural productivity under trade liberalization also is made through the promotion of large farm enterprises. Many officials in the MOA consider this effort as one of important forces that may help to restructuring China’s agriculture, expand agricultural markets, and increase farmer income. Recently fiscal authorities have supported this effort by making grants and allowing tax reductions for the infrastructure investments of the farms. They also have provided large farms with credit subsidies for input procurement and the financing of their efforts to update their technology at all levels of the food change . As a result of China’s WTO accession, the support in this area is expected to increase. However, more effort in the future is likely to shift to supplying services that are supposed to be provided by government in areas such as farm infrastructure development, technology adoption, and extension, rather than direct intervention and subsidy. As subsidies through agricultural investment and inputs in China are subject to WTO restrictions on Aggregate Market Support , it is not expected that the extent of these subsidies will restrict such support. China will be limited to its support of large farms to levels that do not exceed its de minimis level of AMS of 8.5%.

However, it is much more likely that its ability to finance agricultural subsidies will be more binding than the WTO-imposed rules. The other major attempt to increase farm productivity and agricultural competitiveness under trade liberalization is to promote the development of farmer organizations. The government has now officially cast its support for self-organized farmer groups that focus agricultural technology and marketing . At one time, the creation of farmer organization was a political sensitive issue. Leaders were concerned with the rise of any organization outside the government’s authority. Such restrictions, however, caused a dilemma in reforming the nation’s agricultural and rural economies. Policy makers also are aware that with the small scale of China’s farms there are many increases in economic efficiency that might be gained by the creation of effective rural organizations and that if they were successful in raising incomes, there might be a rise in political stability.It is on this basis, then, that leaders have now decided to allow the organization of China’s 240 million farms. Letting these millions of small farmers competing in a market with globalization requires substantial institutional reforms on farm organization and provisions of government service such as technology extension and marketing information and quality controls. It will be in these areas that farmer organizations will be encouraged. In addition, these types of farm organizations that are supported by the government fall under WTO’s “Green Box” categorization and investments to create such groups will not be counted as part of the nation’s AMS measures. Perhaps more than anything, the government is going to need these farmer organizations to lead the fight against the imposition of trade barriers on China’s agricultural exports. Because China’s producers have not been organized, when foreign countries, such as Japan, Korea, and the US have levied trade barriers, typically citing dumping. Even when such cases were based on questionable bases, China had no one who had an incentive or ability to contest the cases. Since China Provisions of anti-dumping and safeguards measures against China’s products, such cases will not abate and the nation needs to have a way to protect the interest of those seeking to export.The financial sector has reformed more slowly than some other sector, and government maintains strong control . Among the commitments regarding the banking sector, the Protocol requires China to open the country’s financial markets in a step by step way. The liberalization must allow foreign competition across wider and wider regions and customer base. After a four years transition period, all regional restrictions will be removed and foreign banks will receive national non-discriminative treatment in the area of banking services. Specifically, restrictions on branch banking can not be imposed.

Those with no money whatsoever were given an allowance and low interest loans subsidized by SOPRO, and housing that the society had leased provided the refugees with much needed shelter. Society funds also paid for the establishment of a twenty-bed hospital and a small home and kindergarten for the children in the capital city. To the southeast of the capital, in the city of Cochabamba, elevation 8,500 feet, SORPRO established a home for the elderly and a sanatorium for those seeking relief from the extreme altitude of the capital.Agricultural training centers were set up to teach refugees the rudiments of farming in a semitropical and tropical environment. Many had no notion of what it meant to work the land; their ranks were filled with professionals such as chemists, engineers, lawyers and physicians. Indeed, in a letter to Hochschild, the Joint’s Paul Baerwald emphasized the importance of success, yet Baerwald also had his deep-seated doubts. “Jewish farm settlement is a much more difficult problem than settlement of peasants.” The Jewish refugee needed both acclimatization and ‘psycho-physiological retraining and readjustment’ Baerwald emphasized. Indeed, besides acclimatizing, one also had to acculturate to a largely indigenous populace, people whose customs and food were exotic in the extreme.There existed elements of the Bolivian population who viewed the newcomers as trespassers who took work from the native, that the displaced refugee was creating the displaced native. Yet there were those who also proffered the hand of friendship in the dynamic relationship between foreigner and native. However, cordial relationships between the refugee and the native could not make up for the shortcomings of the settlement plan. The scholar León E. Bieber lists the factors that led to the abandonment of Buena Tierra,plant benches and notes that a combination of reasons was responsible for its ultimate failure. Among them were the “negligencia en la selección de los inmigrantes.” Many ofthe refugees were professionals, and during the interview process had lied about their backgrounds and level of experience as farmers.

The pressure to escape the Nazis and save one’s life was just too much; hence there were physicians, engineers and other professionals who had falsely claimed an agricultural background. Other key reasons that Bieber noted were the “factores topográficos, la calidad de las tierras, la precaria estructura vial boliviana y la falta de adecuado apoyo.” The sheer isolation of Buena Tierra and the Yungas was due to a paucity of roads and railroad lines into the region. Had this transportation network infrastructure been in place, Buena Tierra may have prospered.The quality of the land and soil at Buena Tierra was hyped by the agronomist Bonoli, who, it turned out, “was profoundly mistaken.”Bieber cited the lack of sufficient government help as another factor that contributed to Buena Tierra’s demise. To achieve success it was essential to have, besides aid from Jewish philanthropies, the full support of the Bolivian Government. Finally, most of the refugees viewed Buena Tierra, and the host country Bolivia, as a stepping stone to other, more enticing locales such as the United States or Argentina, two American countries with thriving Jewish communities. We can debate the failure of the ‘experiments’ at Sosúa and Buena Tierra, yet there was for sure relative success. Both colonies were founded as places of refuge for thousands of involuntary emigrants fleeing the violence of their homeland. The fact that some refugees were able to escape the Nazi death-grip and begin life anew as farmers in distant lands underscores that very success. In retrospect, both projects had achieved their original goal of saving lives, and have left a model from which one may draw conclusions regarding their failure or a success. The model of agricultural settlement that Rosen successfully used for the Ukraine and Crimea, proved to be difficult to transfer to Bolivia and the Dominican Republic. In spite of Rosen’s agronomist background, Rosenberg’s well-placed connections and professional experience as a lawyer, Hochschild’s wealth, and the help of the military man and Bolivian president Germán Busch, the success of these Jewish agricultural colonies was never assured. It all came down to the individual efforts of a few hardy souls and the collective will of many others behind the scenes. Although the same development model was used among the settlements discussed herein, it is clear that what had worked at one site failed miserably at others.

Competent administration, experimentation with different crops, a willing and able work force, along with the use of cutting-edge technology did not, by any stretch of the imagination, guarantee success. A fascist megalomaniac, a couple of third world dictators, a beloved U.S. President, a Jewish mining magnate and a cast of others, made for some very strange bedfellows indeed. Remove one of these historic figures from the equation, and neither Sosúa nor Buena Tierra would have seen the light of day.As human-converted habitats expand over Earth’s surface, the fate of global biodiversity will depend increasingly on the quality and characteristics of farming landscapes . Agricultural systems vary widely in their ability to support biodiversity, with many species extirpated from some but sustained in others . Additionally, characteristics of the species themselves, evolved over millions of years, may predispose some lineages to benefit from human environmental impacts . Phylogenetic diversity, the total evolutionary history or phylogenetic branch lengths of all species in a community , is recognized as having intrinsic conservation value . Also, ecological experiments in small plots indicate that communities with more phylogenetic diversity are more stable , possess higher productivity , and support more species at other trophic levels . Despite the known impact of agriculture on species loss, how habitat conversion affects phylogenetic diversity remains unknown. Studies of plants and invertebrates have established that local environmental disturbances favor subsets of closely related clades and often result in phylogenetic diversity loss . Further, some studies that examine the global extinction risk of birds and mammals suggest that particular branches of the tree of life are at greater risk than others , although whether evolutionarily distinct species are more at risk than species with many living relatives remains contested . We quantified changes in phylogenetic diversity across multiple landscapes in Costa Rica, combining a recent complete avian phylogeny with temporally and spatially extensive tropical bird censuses to assess how habitat conversion is restructuring the avian phylogeny . The data set comprised 44 transects, surveyed in wet and dry seasons over 12 years across four regions in two biomes .

Transects were located in three land-use types: forest reserves, diversified agricultural systems, and intensive mono cultures. Compared with intensive mono cultures, diversified agricultural systems had more crop types, complex configurations of vegetation, and substantial surrounding tree cover . Our analysis focused on three unresolved questions. First, do certain bird clades thrive in agriculture, or is this capacity broadly distributed across the tree of life? Second, how much phylogenetic diversity is lost when native forest is replaced with agriculture? Last, are evolutionarily distinct species capable of persisting in agriculture? We found that clades from across the bird phylogeny thrived in agriculture . Affinity for different habitats showed phylogenetic signal, meaning that closely related species were more likely to share habitat preferences than species that were distantly related . The phylogenetic signal was best described by using Pagel’s lambda transformation of the phylogeny , which reduces the degree of correlation of traits between species below the Brownian motion expectation . Although most taxonomic families had species associated with all habitat types, some families tended to affiliate with particular habitats. For example, pigeons, seedeaters, swallows, and blackbirds were agriculture-affiliated,rolling bench whereas trogons, antbirds, ovenbirds, and manakins were forest-affiliated . Despite the variety of lineages that were found in agriculture, average within transect phylogenetic diversity was 40% lower in intensive mono cultures than in forest reserves and 15% lower in diversified agricultural systems than in forest reserves . Across all transects and years, forest reserves, diversified agricultural systems, and intensive mono cultures housed 4.10, 3.85, and 3.26 billion years of evolutionary history. Two processes were responsible for changes in phylogenetic diversity: species loss and increasing species relatedness. We found roughly the same number of bird species in diversified agricultural systems as in forest reserves [N = 62 species; likelihood ratio test P = 0.75] but half as many species in intensive mono cultures . However, species loss alone did not account for declining phylogenetic diversity in agriculture . Species in forest reserves were less related to one another than expected by chance, whereas species in diversified agricultural systems and intensive mono cultures were more closely related . These patterns indicate that phylogenetic diversity loss in agriculture occurs in two steps. First,habitat conversion from forest to diversified agricultural systems causes a shift in community composition while maintaining species richness: Agricultural species are not nested subsets of forest species . Because species in diversified agricultural communities are closely related, this shift results in a moderate decline in phylogenetic diversity within a transect. Then, as agricultural practices intensify, species loss from this agricultural bird community causes another more-substantial decline in phylogenetic diversity. Whether phylogenetic diversity loss will substantially reshape the global tree of life depends on the capability of species from evolutionarily distinct lineages to persist in agricultural lands. We quantified each species’ evolutionary distinctness as its contribution to the phylogenetic diversity of the world’s 9993 bird species . Species in forest reserves had slightly greater average evolutionary distinctness than those in diversified agricultural systems or intensive mono cultures . This pattern did not result from a small number of highly distinct, forest affiliated species—repeating the analysis after removing the top 10% most distinct species did not alter results .

Conversely, communities in intensive mono cultures hosted younger species with more-rapid diversification rates  . At the species level after accounting for phylogenetic covariance, DR was negatively correlated with forest affiliation and positively correlated with affinity for diversified agricultural systems . To explore how habitat conversion affects the temporal population dynamics and local extirpation risks of evolutionarily distinct species, we developed a temporal, multi-species, hierarchical occupancy model that accounted for detection bias . The model provided a dynamic assessment of which species were extirpated from and/or recolonized sites at the greatest rates from year to year . Extirpation was estimated as the probability that a species did not persist from one year to the next, whereas colonization was the probability that a species was absent one year but present the next. We modeled occupancy dynamics over 12 years, validating our model through examining dry and wet season surveys separately. We found that extirpation probability was highest in intensive mono cultures and lowest in forest reserves . More evolutionarily distinct species experienced higher extirpation rates than less-distinct species in both diversified agricultural systems and intensive mono cultures. Evolutionarily distinct species fared worst in intensive mono cultures, where the top 10% most distinct species experienced extirpation rates ~two times greater than in diversified agricultural systems. Between-year colonization probabilities were low in all land-use types, but evolutionarily distinct species colonized both diversified agricultural systems and intensive mono cultures less frequently than less-distinct species . Repeating analyses at the genus level confirmed that the results were not driven by a few clades. These findings suggest that, over time, evolutionarily distinct species will face challenges in maintaining populations in agricultural systems, especially in intensive mono cultures. We offer two possible explanations for why evolutionarily distinct species and phylogenetic diversity should decline in agriculture. First, species that today inhabit tropical agriculture may have evolved primarily in open habitats, such as grasslands. During geologically recent periods of glaciation when open grassland habitats in the Neotropics proliferated , several clades may have undergone increased speciation , leading to the enrichment of younger species in agriculture . Indeed, we found that species that use natural open habitats were more likely to thrive in agriculture . However, whereas species associated with savannas had slightly higher diversification rates than other species , there were no consistent differences in diversification rates between species that use natural open habitats and those that do not . Another explanation is that birds in agriculture represent a novel community.

We explore sensitivity to spatially heterogeneous future impacts by incorporating a subset of the 35 yield change scenarios from the AgMIP global gridded crop model intercomparison study into GCAM’s exogenous agricultural assumptions. The scenarios selected have been found to span a range of global impacts in previous GCAM versions. Each GGCM assesses the effects on yields of RCP 8.5 changes in CO2 , temperature, and precipitation from five bias-corrected global circulation models for rainfed and perfectly irrigated versions of crops. Water for irrigation is constrained in the version of GCAM used for analysis. We apply crop impacts in one of two ways: only in the U.S. and everywhere . This work is consistent with earlier work using different models to consider similar scenarios regarding the importance of international impacts, adding robustness to the findings through the use of a different multi-sector model. We find that international impacts could be as important as domestic impacts for the financial value of U.S. agricultural crop production in across spatially and temporally varying agricultural impacts scenarios. Crucially, there are scenarios in which examining only domestic impacts would lead to a fundamentally different analysis of the future of U.S. agricultural crop production than if one considered the combined effects of domestic and international impacts. Therefore, while there is uncertainty about climate impacts on future crop yields, evidence suggests that the importance of international effects on the financial value of U.S. crop production is robust across this uncertainty.GCAM couples human and physical Earth systems to explore the impacts of economic and environmental policies. GCAM is calibrated to historical data through 2010 and then simulates forward from 2010 to 2100 in five year time steps by incorporating changes in quantities such as population, GDP, technology, and policy to produce outputs that include land use, emissions, and commodity prices. Specifically, GCAM can assimilate high spatial resolution information on the global distribution of crop yields and analyze its effects on the coupled system of global agriculture markets. This and previous versions of GCAM track long term trend behavior rather than inter annual variability . All scenarios in this study follow Shared Socioeconomic Pathway 2,vertical farm tower the “middle-of-the-road” socio-economic scenario.

The associated GCAM scenario provides the reference against which we measure impacts in this study. GCAM represents the energy system in 32 economic regions, and it represents global production in 384 agricultural and land-use regions. Each of the 384 land units in GCAM represents a water basin-economic region combination. Twenty-three of these lie within the U.S. With each land unit, GCAM allocates land across more than a dozen types based on cover and use. Allocation is based on a logit formulation to optimize profitability, with details provided in Wise et al . Important to this study, GCAM models production of a range of agricultural commodities , each with four different management types . S1 Fig illustrates the land competition nests used by GCAM v5.2 in each GCAM land unit . S1 Table in the S1 File provides the mappings between crops in the FAOSTAT database and GCAM commodities. Some GCAM commodities are straightforward , but some GCAM commodities are economic aggregates, such as Misc Crop and OilCrop . The span of GCAM commodities allows a relatively full modeling of the agricultural sector of economies. On the supply side of agricultural crop production in GCAM, the “no impacts” baseline yield change assumptions are read in exogenously. These yield changes are used by GCAM to calculate the profitability of a GCAM crop-irrigation-fertilizer combination in each GCAM land unit at each time step. This profitability determines land allocated to each land type . The combination of exogenous yields and endogenous land allocation gives production of each crop-irrigation-management combination in each land unit. Because land shares allocated to rainfed versus irrigated, high versus low fertilizer versions of each crop may change, the aggregate yield for each crop output by GCAM will differ from the input yields. In other words, there is endogenous yield intensification in GCAM. AgMIP yield impacts are incorporated as multipliers on the exogenous yield assumptions used by GCAM .The GCAM food demand system creates a slightly elastic portion of demand for each crop type, based on the exogenous population and GDP assumptions GCAM takes as inputs. Therefore, the minimum quantity defined by the food demand system must be met globally by GCAM agricultural crop production.

Other demand sectors are more elastic. For some GCAM agricultural commodities, such as Corn and Oil Crop, this leads to an overall more elastic total demand function because GCAM explicitly models the energy sector and the accompanying price-sensitive demand for use of these crops as bio-fuels. Crops such as Wheat and Rice that are primarily used to feed humans have nearly perfectly inelastic demand. This lends an extra layer of difficulty to analysis of the dynamics in any particular agricultural impacts scenario. Since both supply and demand schedules may shift, there is never a single mechanism that may be identified for any particular price change. GCAM includes mechanisms on both the supply and demand side that allow for adaptation behavior. Specifically, because there is a price elasticity of demand for GCAM agricultural crop commodities , reduction of quantity demanded is one available mechanism in the model. This includes changing demand for animal feed and bio-energy in response to changing prices. On the supply side, economic agents can endogenously adjust land allocation in response to changes in profit rates between rainfed or irrigated versions of crops . Additionally, the option to shift to higher nitrogen fertilizer per unit of land is included, which leads to an increase in both the yield and the cost per unit of land . Pesticide use is currently not explicitly modeled, and therefore changes to pesticide use are not included as an adaptation option in GCAM. Finally, GCAM includes trade of agricultural crop commodities and the ability for producers to shift land allocation among commodities as profit rates change with yield and price changes. A significant difference between the version of GCAM used in this paper and the version documented in detail, is that GCAM 5.2 features different agricultural crop trade behavior. Previously, GCAM modeled completely flexible trade with nearly all agricultural commodities traded freely on a global market with no explicit distinction between domestically-produced and imported products and a common global market price. The version of GCAM used for this study employs a system that is based on an Armington distinction between domestically-produced and imported products . In this new approach in GCAM, we specify region-specific agricultural markets at the 32-region level. Regional demand is an explicitly modeled choice between domestic production and imports from the global market via a nested logit structure, similar to our modeling of land use allocation in GCAM .

International trade is not modeled as explicit bilateral trade but instead as a single market for each commodity that contains all regional gross imports and gross exports. Additional details may be found in the S1 File.We examine the implications of agricultural impacts to U.S. producers through the use of four varying crop-climate model combinations in the AgMIP/ISIMIP global gridded crop model inter comparison study CO2 fertilization effects, driven by RCP 8.5 earth system changes. RCP 8.5 is a climate scenario that features large changes in temperature, CO2, and precipitation and therefore relatively larger local changes in crop yields produced by crop models than under the other RCPs. It was selected with the idea that the larger yield change signal in RCP 8.5 would aid in identifying the emergent mechanisms that dictate the future economic changes resulting from yield changes. The other RCPs are deserving of future examination, but one would expect that the policies applied to reduce emissions would then interact with the impacts of correspondingly smaller yield changes. The GCAM reference scenario to which these crop yield impacts are applied has no climate policies in place. We use AgMIP global gridded yields from each of the EPIC and LPJmL crop models driven by five GCMs under RCP 8.5. Each scenario models yields for different collections of crops. Further, these collections of crops differ from the commodities modeled in GCAM. Yield data is available for both perfectly irrigated and locally rainfed versions of each crop. These gridded yields are aggregated to the GCAM land units using MIRCA2000 harvested area data for weighting. At the basin scale, time series of yields are converted to multipliers by dividing by the historical baseline average yield for each crop-irrigation-basin combination. Finally, these crop-irrigation-basin specific multipliers are converted from the crop model specific crop types to multipliers for each GCAM commodity irrigation-basin combination using GTAP harvested area weights from the GCAM data system for aggregation. This method for incorporating climate driven yield changes as multipliers on GCAM’s exogenous yield assumptions follows methods used in the broader literature. GCAM’s biomass crop commodity receives the median of impacts to all other commodities,vertical plant tower for each irrigation-management practice combination, in each basin. Fig 1 is a schematic summarizing this processing pipeline. S2 Table in S1 File lists the mapping between AgMIP model crops and GCAM commodities used to estimate multipliers. Corresponding water supply constraints for irrigated crops are used for each scenario. Impacts are not applied to grassland or forest to isolate the role of impacts on crop yields.Results are presented for a variety of physical and economic output variables from GCAM. Due to both the variability and the relatively small sample size of spatially heterogeneous climate-crop impact scenarios available for this paper, summary statistics across scenarios are not presented. Rather, the results presented here focus on relationships between the Domestic and the Full scenarios that emerge in each of the different climate-crop impact combinations considered.

To this end, Figs 2 and 3, and S3 Fig illustrate the percent change relative to the no impacts reference GCAM scenario for several economic and physical variables in 2050 at the aggregate U.S. level for different GCAM commodities, under for all scenarios in the work. Fig 2 focuses results on Corn and OilCrop, globally important commodities for which the U.S. is a major producer. Fig 3 presents results for Rice and Wheat, important commodities with more spatially distributed production across the globe. S3 Fig presents results for the remaining GCAM commodities. The variables plotted include Area allocated to each commodity , the U.S. commodity Price, the production Prod of each commodity , and the aggregate endogenous yield change EndYld . Results are also included for changes in revenue Revto examine an aggregate direction of change between Price and Prod, as both variables are important to U.S. producers.Price changes are a primary economic mechanism through which international yield changes are transmitted to U.S. agricultural producers. Because agricultural commodities are traded across the globe, the prices of these commodities are affected by events that occur both in the U.S. and internationally. An event that affects U.S. yields will have consequences for U.S. production but also for prices in the rest of the world and will therefore affect production throughout the world agricultural system. The reverse also holds. Because the U.S. is a significant but not majority contributor to the global agricultural market for most crops, the magnitude of U.S. price changes in the Domestic is consistently smaller than in the Full scenario, across all climate-crop impact combinations for all GCAM commodities, illustrated in Figs 2 and 3 and S3 Fig. This is regardless of whether prices have increased or decreased relative to the baseline scenario, consistent with many of the results reported. Further, applying impacts in the Full case versus in the Domestic results in differing price changes from reference, even for commodities such as Corn that display very similar values in the Full and Domestic cases for physical variables such as area, production, and endogenous yield. It is possible that the relationship between the Full impacts price change from reference and the Domestic impacts price change from reference may break down as the structure of the system being modeled fundamentally changes. For example, in a more restrictive trade scenario, U.S. producers would be more restricted in their options to respond to future climate and prices would be increasingly dictated solely by the direct impacts on U.S. productivity. For commodities such as Corn and Oil Crop, for which the U.S. is a significant producer and exporter, a major shock only to U.S. production is closer in magnitude to a shock across the entire world than it would be for other commodities.