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The Century model was used to simulate soil C and N cycling and crop produc- tion dynamics in an ongoing field experiment in Uruguay (started in 1963). The model was calibrated using observed data from three treatments (crop or crop–pasture rotations) and validated with a fourth treatment. The model correctly predicted the impact of different treatments on microbial biomass, N mineralization, soil respi- ration, and crop yields. The model and observed data show that soil respiration, N mineralization, soil C, and crop yields increase with increasing plant-derived C inputs caused by increasing the frequency of pastures in the rotations. This is one of the first papers that show the strong positive correlation of observed soil C with plant C soil inputs to field-observed microbial biomass, soil respiration, and N mineralization. The results also showed that reducing tillage and transitioning to a no-till system increased soil C and reduced soil erosion. The main path of soil C losses was het- erotrophic microbial respiration, which accounted for 66% of the total C lost in a continuous crop rotation and no fertilizers, 71% in a continuous crop rotation with fertilizers, and 86% in a crop–pasture rotation with fertilizers. Model results from a degraded cropping system showed that adding grass–clover (Trifolium spp.) pastures greatly increased plant production and soil C, whereas reducing the frequency of grass–clover pastures in high-fertility cropping systems from 50% of the time to 25% reduces crop yields and soil C. Including cover crops substantially increases crop production and maintains soil C in high-fertility and degraded cropping systems.

While many Madden–Julian Oscillation (MJO) teleconnections are well documented, the significance of these teleconnections to agriculture is not well understood. Here we analyze how the MJO affects the climate during crop flowering seasons, when crops are particularly vulnerable to abiotic stress. Because the MJO is located in the tropics of the summer hemisphere and maize is a tropical, summer-grown crop, the MJO teleconnections to maize flowering seasons are stronger and more coherent than those to wheat, which tends to be grown in midlatitudes and flowers during the spring. The MJO significantly affects not only daily average precipitation and soil moisture, but also the probability of extreme precipitation, soil moisture and maximum temperatures during crop flowering seasons. The average influence on the probability of extreme daily precipitation, soil moisture, and maximum temperature events is roughly equal. On average the MJO modifies the probability of a 5th or 95th, 10th or 90th, and 25th or 75th percentile event by $$\sim $$∼ 2.5%, $$\sim $$∼ 4% and $$\sim $$∼ 7%, respectively. This means that an exceptionally dry (10th percentile) soil moisture value, for example, would become $$\sim $$∼ 40% more common (happening 14% of the time) during certain MJO phases. That the MJO can simultaneously dry soils and raise maximum air temperatures may be particularly damaging to crops because without available soil water during times of heat stress, plants are unable to transpire to cool leaf-level temperatures as a means of avoiding long-term damage. As a result, even though teleconnections from the MJO last only a few days to a week, they likely affect crop growth.

Agricultural intensification in Latin America has led to accelerated soil erosion, water pollution and food with pesticide residues, which are all signs of unsustainable development. In Uruguay, agricultural intensification with continuous cropping has threatened the country’s primary natural resource: its soil. At the same time, incentives for further intensification and specialization are high, since particularly soybeans have offered the highest (short-term) economic margins. This paper aims to contribute to the discussion about governance for sustainable development through an in-depth critical examination of the main flagship public policy response in Uruguay to soil degradation: the Soils Use and Management Plans (SUMP). SUMP indeed has managed to change cultivation practices in a more sustainable direction. The analysis shows that the relative success of SUMP is partly due to its experimental policy design which has allowed for collective knowledge construction and reflexive learning. It also shows that Uruguay’s long history of accumulated domestic soil expertise and state intervention rendered trust in the regulative process among producers and ultimately a high degree of acceptance. Nevertheless, while this policy is found innovative and promising, there is still a need for improvement of governance designs, if genuinely sustainable development is to be achieved.

Simultaneous yield shocks in multiple breadbaskets pose a potential threat to global food security, yet the historical risks and causes of such shocks are poorly understood. Here, we compile a dataset of subnational maize and wheat yield anomalies in 25 countries dating back to 1900 to better characterize the past, present, and future risk of multiple breadbasket shocks. We find that years in which at least half of all maize or wheat breadbaskets fall 10% (5%) below expected yields has occurred in ~2-3% (~14-16%) of years over the last century. Importantly, multiple breadbasket shocks have been decreasing in frequency from 1930 to 2017. The El Niño Southern Oscillation (ENSO) most strongly affects the probability of multiple maize breadbasket shocks, while the North Atlantic Oscillation (NAO) most strongly affects the probability of multiple wheat breadbasket shocks, each influencing the probability by up to 40%. The effect of climate change on climate stress in maize and wheat breadbaskets is mixed; extreme heat will increase uniformly, agricultural soil moisture stress will remain constant or increase, but hydrological stress (as measured by runoff) will remain constant or decrease in breadbasket regions.

In the original version of the article contained errors in the fig 3 and 4 and captions of Figures 3 and 4 with respect to the panels in the top row.

Managing climate-related risks is one of the key components of enhancing agricultural resilience and productivity under increasingly variable and changing climate. In this study, a decision support tool, SIMAGRI, was developed to translate climate information into agricultural and economic terms that can support strategic and tactical decisions in crop production e.g., crop choices, agricultural management practices, insurance needs, among others. The SIMAGRI helps users to run crop simulation models for three target crops (maize, soybean and wheat) with representative management options, based on historical climate data and probabilistic seasonal climate forecasts. The user-friendly interface of SIMAGRI allows users to compare results of several management practices (e.g., planting dates, crops and cultivars, fertilizer/irrigation application, etc.) given a climate condition, and thus help identify appropriate mixes of production options, which can increase (reduce) positive (negative) impacts of current and future climatic conditions. The SIMAGRI can be used for different purposes (e.g., initial assessment or operational use for generating crop yield outlook, etc.). It can enhance better communications with stakeholders, and for identifying their needs.

The El Nino Southern Oscillation (ENSO) is a major source of interannual climate variability. ENSO life cycles and the associated teleconnections evolve over multiple years at a global scale. This analysis is the first attempt to characterize the structure of the risk posed by trans-Pacific ENSO teleconnections to crop production in the greater Pacific Basin region. In this analysis we identify the large-scale atmospheric dynamics of ENSO teleconnections that affect heat and moisture stress during the growing seasons of maize, wheat and soy. We propose a coherent framework for understanding how trans-Pacific ENSO teleconnections pose a correlated risk to crop yields in major agricultural belts of the Americas, Australia and China over the course of an ENSO life cycle by using observations and a multi-model ensemble of climate anomalies during crop flowering seasons. Trans-Pacific ENSO teleconnections are often (but not always) offsetting between major producing regions in the Americas and those in northern China or Australia. El Ninos tend to create good maize and soybean growing conditions in the US and southeast South America, but poor growing conditions in northern China, southern Mexico and the Cerrado in Brazil. The opposite is true during La Nina. Wheat growing conditions in southeast South America generally have the opposite sign of those in Australia. Furthermore, multi-year La Ninas can force multi-year growing season anomalies in Argentina and Australia. Most ENSO teleconnections relevant for crop flowering seasons are the result of a single trans-Pacific circulation anomaly that develops in boreal summer and persists through the following spring. During the late summer and early fall of a developing ENSO event, the tropical Pacific forces an atmospheric anomaly in the northern midlatitudes that spans the Pacific from northern China to North America and in the southern midlatitudes from Australia to southeast South America. This anomaly directly links the soybean and maize growing seasons of the US, Mexico and China and the wheat growing seasons of Argentina, southern Brazil and Australia. The ENSO event peaks in boreal winter, when the atmospheric circulation anomalies intensify and affect maize and soybeans in southeast South America. As the event decays, the ENSO-induced circulation anomalies persist through the wheat flowering seasons in China and the US.

CONTEXT Global climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVE We aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODS We used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620–668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONS The IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003–2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December–January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. SIGNIFICANCE Here we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season.

Framed experiments and games are a useful medium to understand how context affects individual and group decision-making. They are particularly relevant for field research in agriculture, where alternative experimental designs can be costly and unfeasible. After a systematic review of the literature, we found that the volume of published studies employing coordination and cooperation games increased during the 2000-2020 period. In recent years, there has been greater attention given to natural resource management, conservation, and ecology areas, especially in strategic regions for agriculture sustainability. Other games, such as trust and risk games, have come to be regarded as standards of framed field experiments in agriculture. Regardless of sectoral focus, most games' results are subject to internal and external validity criticism. In particular, a significant portion of the games showed potential recruitment biases against women and no opportunities for a continued impact assessment. However, games' validity should be judged on a case-by-case basis. Specific cultural aspects of games might reflect the real context, and generalizing games' conclusions to different settings is often constrained by cost and utility. Overall, games in agriculture could benefit from more significant, frequent, and inclusive experiments and data – all possibilities offered by digital technology. Present-day physical distance restrictions may accelerate this shift. New technologies and engaging ways to approach farmers might represent a turning point for games in agriculture in the 21st century.

Understanding what causes weather-related stresses that lead to crop failures is a critical step towards stabilizing global food production. While there are many sources of weather-related stresses...

In many regions around the world, Regional Climate Outlook Forums (RCOFs) provide seasonal climate information and forecasts to decision-makers at regional and national levels. Despite having two decades of experience, the forums have not been systematically monitored or evaluated. To address this gap, and to better inform nascent and widespread efforts in climate services, the authors propose a process-oriented evaluation framework derived from literature on decision support and climate communication around the production and use of scientific information. The authors apply this framework to a case study of the Caribbean RCOF (CariCOF), where they have been engaged in a collaborative effort to integrate climate information and decision processes to enhance regional climate resilience. The authors’ examination of the CariCOF shows an evolution toward the use of more advanced and more diverse climate products, as well as greater awareness of user feedback. It also reveals shortfalls of the CariCOF, including a lack of diverse stakeholder participation, a need for better understanding of best practices to tailor information, undeveloped market research of climate products, insufficient experimentation and vetting of communication mechanisms, and the absence of a way to steward a diverse network of regional actors. The authors’ analysis also provides insight that allowed for improvements in the climate services framework to include mechanisms to respond to changing needs and conditions. The authors’ process-oriented framework can serve as a starting point for evaluating RCOFs and other organizations charged with the provision of climate services.

Climate-smart agriculture (CSA) is widely promoted as an approach for reorienting agricultural development under the realities of climate change. Prioritising research-for-development activities is crucial, given the need to utilise scarce resources as effectively as possible. However, no framework exists for assessing and comparing different CSA research investments. Several aspects make it challenging to prioritise CSA research, including its multi-dimensional nature (productivity, adaptation and mitigation), the uncertainty surrounding many climate impacts, and the scale and temporal dependencies that may affect the benefits and costs of CSA adoption. Here we propose a framework for prioritising agricultural research investments across scales and review different approaches to setting priorities among agricultural research projects. Many priority-setting case studies address the short- to medium-term and at relatively local scales. We suggest that a mix of actions that span spatial and temporal time scales is needed to be adaptive to a changing climate, address immediate problems and create enabling conditions for enduring change.

Most countries lack effective policies to manage climate risks, despite growing concerns with climate change. The authors analyzed the policy evolution from a disaster management to a risk management approach, using as a case study four agricultural droughts that impacted Uruguay’s livestock sector in the last three decades. A transdisciplinary team of researchers, extension workers, and policy makers agreed on a common conceptual framework for the interpretation of past droughts and policies. The evidence presented shows that the set of actions implemented at different levels when facing droughts were mainly reactive in the past but later evolved to a more integral risk management approach. A greater interinstitutional integration and a decreasing gap between science and policy were identified during the period of study. Social and political learning enabled a vision of proactive management and promoted effective adaptive measures. While the government of Uruguay explicitly incorporated the issue of adaptation to climate change into its agenda, research institutions also fostered the creation of interdisciplinary study groups on this topic, resulting in new stages of learning. The recent changes in public policies, institutional governance, and academic research have contributed to enhance the adaptive capacity of the agricultural sector to climate variability, and in particular to drought. This study confirms the relevance of and need to work within a transdisciplinary framework to effectively address the different social learning dimensions, particularly those concerning the adaptation to global change.

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season.

Uruguay is pursuing renewable energy production pathways using feedstocks from its agricultural sector to supply transportation fuels, among them ethanol produced from commercial technologies that use sweet and grain sorghum. However, the environmental performance of the fuel is not known. We investigate the life cycle environmental and cost performance of these two major agricultural crops used to produce ethanol that have begun commercial production and are poised to grow to meet national energy targets for replacing gasoline. Using both attributional and consequential life cycle assessment (LCA) frameworks for system boundaries to quantify the carbon intensity, and engineering cost analysis to estimate the unit production cost of ethanol from grain and sweet sorghum, we determined abatement costs. We found: 1) an accounting error in estimating N2O emissions for a specific crop in multiple crop rotations when using IPCC Tier 1 methods within an attributional LCA framework, due to N legacy effects; 2) choice of baseline and crop identity in multiple crop rotations evaluated within the consequential LCA framework both affect the global warming intensity (GWI) of ethanol; and 3) although abatement costs for ethanol from grain sorghum are positive and from sweet sorghum they are negative, both grain and sweet sorghum pathways have a high potential for reducing transport fuel GWI by more than 50% relative to gasoline, and are within the ranges targeted by the US renewable transportation fuels policies. This article is protected by copyright. All rights reserved.

In this analysis we show how globally coherent teleconnections from life-cycles of the El Nino Southern Oscillation (ENSO) lead to correlated crop production anomalies in North and South America. We estimate the magnitude of ENSO-induced Pan-American production anomalies and discuss how increasing crop harvesting frequency may affect Pan-American production variability. We find that ENSO accounts for ∼72%, 30% and 57% of Pan-American maize, soybean and wheat production variability, respectively. ENSO-induced production anomalies are greatest for maize, with median anomalies of ∼5% of Pan-American production. ENSO-induced yield anomalies for maize and soybeans tend to be of the same sign in North America and southeast South America but of an opposite sign in northeast Brazil. Teleconnections for wheat are more complicated because ENSO affects wheat yields via lagged soil moisture teleconnections in the US and an increased probability of disease in South America, but anomalies tend to be of the same sign in North America and southeast South America. After broadly characterizing ENSO-induced production anomalies, we demonstrate that they are not static in time. Increasing crop harvesting frequency has affected the correlated risks posed by ENSO. We use a soil water balance to show that in Brazil changing to a safrinha cropping cycle increases both the mean water stress and the ENSO-induced soil water content anomalies during flowering in both the maize and soybean seasons, which is a result of increasing evaporative demand during times of lower precipitation and moving the flowering seasons into months with strong ENSO teleconnections. Increasing crop harvesting frequency in Brazil has therefore increased ENSO-induced production variability of soybeans and maize.

The characteristic evolution of El Nino Southern Oscillation (ENSO) on timescales of months to years means that risks to agriculture have structure between seasons and years. The potential for consecutive ENSO-induced yield anomalies is of particular interest in major food producing areas, where modest changes in yield have significant effects on global markets. In this study, we analyse how multi-year El Nino and La Nina life cycles relate to climate sensitive portions of major crop-growing seasons in North and South America. We analyse the dynamics underlying these life cycles to illustrate which aspects of the system are most important for agriculture. In North America, the same-season teleconnections affecting soybean and maize have been well studied, but we demonstrate the importance of lagged soil moisture teleconnections for wheat in the southern Great Plains. In South America, peak ENSO sea surface temperature (SST) teleconnections are concurrent with, and therefore critical for, wheat and maize growing seasons while soil moisture memory in Argentina plays an important role during the soybean growing season. Finally, we show that ENSO teleconnection life cycles are consistent with historical yield anomalies. Both El Nino and La Nina life cycles tend to force consecutive seasons of either above or below expected yields. While the magnitude of the yield anomalies forced by ENSO is often modest, they occur in major crop-producing regions.

Seasonal climate prediction can potentially contribute to achieving a more resilient cropping system management. This can help alleviate food insecurity and the economical sustainability of farming at large. For this purpose, seasonal climate forecasts are used to generate crop forecasts. This study assesses two methods for linking seasonal climate forecasts with crop models to improve crop yield predictability in the Iberian Peninsula (IP). Crop models usually require daily weather data and therefore, we tested two methods to disaggregate seasonal climate forecasts into daily weather realizations: (1) a conditional stochastic weather generator (predictWTD) and (2) a simple forecast probability resampler (FResampler1).These methods were evaluated under three seasonal rainfall forecasts by analyzing the impacts on rainfed wheat yield and on irrigation requirements and yields of maize crop. In addition, we estimated the gross margins (€ ha− 1) and the production risks associated with contrasting scenarios of seasonal rainfall forecasts (dry and wet). Both methods provided comparable predictability and therefore, both seem feasible options for using seasonal forecasts to establish yield forecasts and irrigation requirements. The large impact of crop prices and of irrigation cost on gross margins for both crops suggests that using a combination of information on expected market prices and crop forecast based on seasonal climate forecasts can be an effective tool for farmer's decision-making, especially under dry forecast situation and/or in locations with low annual precipitation. These methods can help to quantify the benefits and risks from the seasonal weather forecasts to farmers in the IP. The anticipation of risks and the opportunity that skillful climate and crop forecast provide allows for windows of opportunity to prepare and pre-empt mitigating actions., The research by M. Capa-Morocho has been partly supported by a PICATA pre-doctoral fellowship from the Moncloa Campus of International Excellence (UCM-UPM) and the MULCLIVAR project (CGL2012-38923-C02-01 and CGL2012-38923-C02-02).

In countries where livestock production based on native grasslands is an important economic activity, information on structural characteristics of forage is essential to support national policies and decisions at the farm level. Remote sensing is a good option for quantifying large areas in a relative short time, with low cost and with the possibility of analyzing annual evolution. This work aims at contributing to improve grazing management, by evaluating the ability of remote sensing information to estimate forage height, as an estimator of available biomass. Field data (forage height) of 20 commercial paddocks under grazing conditions (322 samples), and their relation to MODIS data (FPAR, LAI, MIR, NIR, Red, NDVI and EVI) were analyzed. Correlations between remote sensing information and field measurements were low, probably due to the extremely large variability found within each paddock for field observations (CV: Around 75%) and much lower when considering satellite information (MODIS: CV: 4%–6% and Landsat:CV: 12%). Despite this, the red band showed some potential (with significant correlation coefficient values in 41% of the paddocks) and justifies further exploration. Additional work is needed to find a remote sensing method that can be used to monitor grasslands height.

Natural grasslands are being progressively degraded around the world due to human-induced action (e.g., overgrazing), but there is neither a widely accepted conceptual framework to approach degradation studies nor a clear definition of what “grassland degradation” is. Most of the drivers, processes, and consequences related to grassland degradation are widespread and are usually separately quoted in the literature. In this paper, we propose a comprehensive framework with different conceptual categories, for monitoring grassland degradation, and a new definition based on current ones. We provide a conceptual update of grassland degradation based on a literature review and an expert survey, focused on the Río de la Plata grasslands (RPG). We identified “drivers” as external forces or changes that cause degradation; “processes” as measurable changes in grasslands conditions that can be evaluated using indicators; and “consequences” as the impacts or results of the process of grassland degradation. We expect that this conceptual framework will contribute to monitoring programs, to support management decisions, to design conservation measures, and to communicate the importance of grasslands conservation and the different concepts involved. Particularly for RPG, we expect that this paper will contribute to promote sustainable management practices in this important and often neglected ecosystem.

Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end‐of‐century projections neglects more pressing development concerns, which relate to the management of shorter‐term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long‐term view genuinely is relevant to decision‐making, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision‐relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change services. This article is categorized under: Climate and Development > Knowledge and Action in Development

Scientific advances in the last decades resulted in the ability to establish probabilistic seasonal climate forecasts (SCF). The availability of climate predictions generated expectations that they would be readily integrated into decision making but their actual assimilation proved to a challenge. This paper discusses lessons learned in the attempt of incorporating SCF in decisions and plans, with the expectation of informing similar applications of sub-seasonal to seasonal forecasts. We propose that successful embedding of SCF into decision-making requires the stakeholders’ awareness of the full distribution of the relevant climate variables. Such familiarity prepares the ground to adequately use climate forecasts, which are a shifted version of the expected climate. A second and more complex step, consists of ensuring stakeholders’ understanding of how those uncertainties propagate through the system of interest and affect outcomes downstream. We discuss common misconceptions that can affect the incorporation of forecasts into decisions. One is that climate forecasts will eliminate climate uncertainties. A second misconception occurs when stakeholders wrongly associate difficulties of decision making under uncertainty to the introduction of a climate forecasts. We propose to frame SCFs as a variation to business as usual, rather than something entirely new. Finally, we argue that successful incorporation of SCF into actual decisions requires strong and sustained interaction between climate scientists and stakeholders in a co-production process within which trust can be established.

The ability to implement climate risk management measures is associated with the availability and effectiveness of climate services to inform decision making. Using the case of agricultural droughts in livestock systems of Uruguay, this paper analyzes the extent in which available climate information is being used for adaptation to droughts. Semi-structured interviews and The Q methodology was applied to farmers, public policy makers and academic researchers. Four different profiles of the use of climate information were obtained: convinced, pragmatic, pessimistic and skeptical. The need of understanding the specific contexts of use of climatic information for tailored climate services elaboration is noted. Translation of information is also necessary, since the lack understanding of the message results in unused information. There was consensus that preventive measure should be taken to minimize the impacts of drought and therefore, developing effective climate services should prioritize preventive measures.

The inherent complexity of numerical models and the diversity of stakeholders in integrated water resources management (IWRM) create challenges in achieving credibility, salience and legitimacy to develop trust in model-based scenarios. In Uruguay, there has been significant debate on increasing agricultural production while managing agriculture’s environmental impacts (e.g., on water quality and environmental flows). This paper reports on the evolution of a stakeholder process in a case study with a multi-institutional participatory modelling group, supported by researchers. This specific participatory modelling (PM) project is unique in that the active stakeholders are the actual hydrological modellers, and the role of “experts” is mainly in facilitation and capacity building. The results highlight the different bottlenecks and the factors that enabled effective collaboration in this PM project. The main bottlenecks were related to: different views on representation of the watershed, the quality and usability of different input data, the public information for the technical implementation of the model, and the priority of output scenarios. The factors that enhanced collaboration were: a focus on a single basin problem, strong support from upper management, and support from experts in coordination and capacity building. The detailed documentation provided with this project can inspire similar approaches in the future.

Games are particularly relevant for field research in agriculture, where alternative experimental designs can be costly and unfeasible. Games are also popular for non-experimental purposes such as recreating learning experiences and facilitating dialogue with local communities. After a systematic review of the literature, we found that the volume of published studies employing coordination and cooperation games increased during the 2000–2020 period. In recent years, more attention has been given to the areas of natural resource management, conservation, and ecology, particularly in regions important to agricultural sustainability. Other games, such as trust and risk games, have come to be regarded as standards of artefactual and framed field experiments in agriculture. Regardless of their scope, most games’ results are subject to criticism for their internal and external validity. In particular, a significant portion of the games reviewed here reveal recruitment biases towards women and provide few opportunities for continued impact assessment. However, games’ validity should be judged on a case-by-case basis. Specific cultural aspects of games might reflect the real context, and generalizing games’ conclusions to different settings is often constrained by cost and utility. Overall, games in agriculture could benefit from more significant, frequent, and inclusive experiments and data—all possibilities offered by digital technology. Present-day physical distance restrictions may accelerate this shift. New technologies and engaging mediums to approach farmers might present a turning point for integrating experimental and non-experimental games for agriculture in the 21st century.

This study examines the joint variability of precipitation, river streamflow and temperature over northeastern Argentina; advances the understanding of their links with global SST forcing; and discusses their impacts on water resources, agriculture and human settlements. The leading patterns of variability, and their nonlinear trends and cycles are identified by means of a principal component analysis (PCA) complemented with a singular spectrum analysis (SSA). Interannual hydroclimatic variability centers on two broad frequency bands: one of 2.5–6.5 years corresponding to El Niño Southern Oscillation (ENSO) periodicities and the second of about 9 years. The higher frequencies of the precipitation variability (2.5–4 years) favored extreme events after 2000, even during moderate extreme phases of the ENSO. Minimum temperature is correlated with ENSO with a main frequency close to 3 years. Maximum temperature time series correlate well with SST variability over the South Atlantic, Indian and Pacific oceans with a 9-year frequency. Interdecadal variability is characterized by low-frequency trends and multidecadal oscillations that have induced a transition from dryer and cooler climate to wetter and warmer decades starting in the mid-twentieth century. The Paraná River streamflow is influenced by North and South Atlantic SSTs with bidecadal periodicities. The hydroclimate variability at all timescales had significant sectoral impacts. Frequent wet events between 1970 and 2005 favored floods that affected agricultural and livestock productivity and forced population displacements. On the other hand, agricultural droughts resulted in soil moisture deficits that affected crops at critical growth stages. Hydrological droughts affected surface water resources, causing water and food scarcity and stressing the capacity for hydropower generation. Lastly, increases in minimum temperature reduced wheat and barley yields.

The unusual severity and return time of the 2005 and 2010 dry-season droughts in western Amazon is attributed partly to decadal climate fluctuations and a modest drying trend. Decadal variability of western Amazon hydroclimate is highly correlated to the Atlantic sea surface temperature (SST) north-south gradient (NSG). Shifts of dry and wet events frequencies are also related to the NSG phase, with a 66% chance of 3+ years of dry events per decade when NSG > 0 and 19% when NSG

The incidence of escaped agricultural fire has recently been increasing in the Western Amazon, driven by climate variability, land use change, and changes in patterns of residency and land occupation. Preventing and mitigating the negative impacts of fire in the Amazon require a comprehensive understanding not only of what the drivers of fire activity are, but also how these drivers interact and vary across scales. Here, we combine multi-scalar data on land use, climate, and landowner residency to disentangle the drivers of fire activity over 10 years (2001–2010) on individual landholdings in a fire-prone region of the Peruvian Amazon. We examined the relative importance of and interactions between climate variability (drought intensity), land occupation (in particular, landowner absenteeism), and land cover variables (cover of fallow and pasture) for predicting both fire occurrence (whether or not fire was detected on a farm in a given year) and fire size. Drought intensity was the most important predictor of fire occurrence, but land-cover type and degree of landowner absenteeism increased fire probability when conditions were dry enough. On the other hand, drought intensity did not stand out relative to other significant predictors in the fire size model, where degree of landowner absenteeism in a village and percent cover of fallow in a village were also strongly associated with fire size. We also investigated to what extent these variables measured at the individual landholding versus the village scale influenced fire activity. While the predictors measured at the landholding and village scales were approximately of equal importance for modeling fire occurrence, only village scale predictors were important in the model of fire size. These results demonstrate that the relative importance of various drivers of fire activity can vary depending on the scale at which they are measured and the scale of analysis. Additionally, we highlight how a full understanding of the drivers of fire activity should go beyond fire occurrence to consider other metrics of fire activity such as fire size, as implications for fire prevention and mitigation can be different depending on the model considered. Drought early warning systems may be most effective for preventing fire in dry years, but management to address the impacts of landowner absenteeism, such as bolstering community fire control efforts in high-risk areas, could help minimize the size of fires when they do occur. Thus, interventions should focus on minimizing fire size as well as preventing fires altogether, especially because fire is an inexpensive and effective management tool that has been in use for millennia.

Study region: An area in NEA located within the La Plata Basin (LPB) where extreme precipitation events (EPE) are the most harmful and costly natural disasters. Study focus: The study analyzes the spatiotemporal behavior of EPE through a Principal Component Analysis (PCA) applied to fields of Standard Precipitation Index (SPI) at different time scales. The vulnerability of the NEA to EPE was estimated by defining the portion of the region experiencing extremely dry/wet conditions in critical months. Singular Spectrum Analysis (SSA) was applied to the time series looking for significant trends or oscillatory modes. New hydrological insights for the region: The spatial average SPI series show a positive nonlinear trend with changes to wetter conditions from 1960 to2000s, subsequent signs of stabilization and a trend reversal since 2007. The most severe hydrological and agricultural droughts occurred between 1901and 1960 while the larger wet EPE were recorded between 1970 and 2005. Moreover, a prolonged dry period was registered between 1921 and 1939 that might extend the “Pampas Dust Bowl” to the bulk of the NEA. The EPE show cycles of 6.5 and 8.7 years together with a quasi-decadal cycle in the Northwestern corner. Our results suggest that almost the entire NEA is highly vulnerable to EPE at a time scale relevant for agricultural activities and the Central-West portion is particularly vulnerable to hydrological EPE. Fil: Lovino, Miguel Angel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Unidad de Investigaciones Hidroclimáticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina Fil: Garcia, Norberto Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Unidad de Investigaciones Hidroclimáticas; Argentina Fil: Baethgen, Walter. Columbia University; Estados Unidos

Os sistemas de produção agrícola enfrentam o desafio de alcançar a intensificação sustentável, ou seja, aumentar a produtividade sem afetar o ambiente de uma forma que possa comprometer o desenvolvimento das gerações futuras. Em muitos países em desenvolvimento a disponibilidade de informações para auxiliar os interessados, tanto os agentes públicos quanto os privados, para atingir esse objetivo, muitas vezes, não é uma limitação crítica. Levar as informações relevantes de institutos de pesquisa para os agentes de extensão, agricultores, agroindústrias e formuladores de política tem sido uma limitação mais frequente. Novos enfoques, ferramentas e sistemas de informação são necessários para efetivamente incorporar o conhecimento gerado nos sistemas de pesquisa em decisões reais e na elaboração de políticas públicas. Estas novas abordagens e ferramentas devem levara em consideração, entender e mapear as redes de conhecimento existentes, através das quais a informação flui e atinge os agentes públicos e privados. Os novos sistemas devem incorporar os avanços científicos e tecnológicos alcançados nas diferentes disciplinas e gerar conhecimento integrado para efetivamente auxiliar políticas e decisões reais. Este artigo descreve o processo que levou ao estabelecimento de um sistema de informação de apoio à tomada de decisão no Uruguai (SNIA) que está sendo liderado pelo IRI em colaboração com INIA e o Ministério da Agricultura. O artigo discute a abordagem geral e os objetivos que o IRI e seus parceiros estão usando para estabelecer o SNIA e introduz algumas das suas características, componentes e recursos. Por último, o artigo apresenta exemplos de informações, produtos e ferramentas que estão sendo incluídas no SNIA.

It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain (NCP) were constructed in terms of stochastic daily weather sequences. A nonhomogeneous hidden Markov model (NHMM) was used to downscale daily precipitation projections at 32 stations during winter wheat and summer maize growing seasons for a baseline (1966-2005) and a 21st century (2080-2099) A1B scenario, using selected general circulation models (GCMs). A climatological seasonal cycle of regional-averaged daily reanalysis precipitation was used as input to the down-scaling for the baseline simulation; this input was then scaled by the precipitation changes from GCM projections to generate down-scaled stochastic simulations of precipitation in the 21st century. Temperature was generated using a weakly stationary generating process, conditional on precipitation occurrence, with 21st century additive changes taken from the GCMs at the regional scale. Three hypotheses about changes in solar radiation (−20%, 0% and 20%) were made considering the large uncertainty in its future change. The down-scaled simulations exhibit station increases in the mean daily rainfall of 13.9-69.7% in the scenarios driven by the GCM with the projected largest and multi-model mean precipitation increase for the wheat season, with changes of 0.4-29.9% for the maize season. In the scenario driven by the GCM with the largest projected precipitation decrease, the simulated rainfall decreases at all stations, with changes ranging from −24.6 to −0.1% for the wheat and maize seasons, respectively. Temperature increases by about 3.7 ◦ C for the wheat season and 3.6 ◦ C for the maize season. Copyright 

Destructive fires in Amazonia have occurred in the past decade, leading to forest degradation, carbon emissions, impaired air quality, and property damage. Here, we couple climate, geospatial, and province-level census data, with farmer surveys to examine the climatic, demographic, and land use factors associated with fire frequency in the Peruvian Amazon from 2000 to 2010. Although our results corroborate previous findings elsewhere that drought and proximity to roads increase fire frequency, the province-scale analysis further identifies decreases in rural populations as an additional factor. Farmer survey data suggest that increased burn scar frequency and size reflect increased flammability of emptying rural landscapes and reduced capacity to control fire. With rural populations projected to decline, more frequent drought, and expansion of road infrastructure, fire risk is likely to increase in western Amazonia. Damage from fire can be reduced through warning systems that target high-risk locations, coordinated fire fighting efforts, and initiatives that provide options for people to remain in rural landscapes.

Grain yields of wheat and maize were obtained from national statistics and simulated with an agricultural system model to investigate the effects of historical climate variability and irrigation on crop yield in the North China Plain (NCP). Both observed and simulated yields showed large temporal and spatial variability due to variations in climate and irrigation supply. Wheat yield under full irrigation (FI) was 8 t ha−1 or higher in 80% of seasons in the north, it ranged from 7 to 10 t ha−1 in 90% of seasons in central NCP, and less than 9 t ha−1 in 85% of seasons in the south. Reduced irrigation resulted in increased crop yield variability. Wheat yield under supplemental irrigation, i.e., to meet only 50% of irrigation water requirement [supplemental irrigation (SI)] ranged from 2.7 to 8.8 t ha−1 with the maximum frequency of seasons having the range of 4–6 t ha−1 in the north, 4–7 t ha−1 in central NCP, and 5–8 t ha−1 in the south. Wheat yield under no irrigation (NI) was lower than 1 t ha−1 in about 50% of seasons. Considering the NCP as a whole, simulated maize yield under FI ranged from 3.9 to 11.8 t ha−1 with similar frequency distribution in the range of 6–11.8 t ha−1 with the interval of 2 t ha−1. It ranged from 0 to 11.8 t ha−1, uniformly distributed into the range of 4–10 t ha−1 under SI, and NI. The results give an insight into the levels of regional crop production affected by climate and water management strategies.

The frequency of Fusarium head blight (FHB) outbreaks increased considerably in many countries in the last two decades. We used a crop-disease model to assess the impacts of climate variability over a 50-year period at Passo Fundo, Rio Grande do Sul, Brazil. Thirty individual simulation runs of a wheat model were performed for each year by varying planting dates from 1 June to 30 June. For each simulation, an FHB risk index was estimated with the disease model using the heading date simulated by the crop model. A seasonal FHB risk index was estimated by averaging the 30 indices each year. Our simulation results showed that climate at Passo Fundo became favourable to FHB after the 1980s following a period of less favourable conditions for epidemics in the 1960s and 1970s. The risk for FHB was higher for the later planting dates in the most recent decades. Consideration of sea surface temperature (SST) anomalies (deviations from long-term mean values) in the Tropical Pacific Ocean allowed the separation of groups of years with frequently low FHB risk (negative SST anomalies, La Nina like years) and years with frequently high FHB risks (positive SST anomalies – El Nino like years, and near-normal SST – neutral years). Southern Atlantic SST anomalies were then included to separate years with different FHB risks in El Nino-like years and Neutral years. However, the consideration of these anomalies did not result in an improvement. Our results suggest that increased spring rainfall linked to warm sea surface temperature in the Pacific after the 1980s was associated with higher frequency of FHB epidemics in southern Brazil.

It goes without saying that most crops are sensitive to variations in weather and climate. When the influence of the El Nino-Southern Oscillation on rainfall for several regions of the world was first discovered climate scientists assumed that this information would be of immediate use by farmers, the general agricultural community, and other communities. One of the reasons this has not happened as quickly and universally as expected is that many users are not able to relate the climate information to their practices. In this paper we illustrate an application of climate information as it relates to the risk of a poor crop yield. Specifically we show how the odds of a good, versus poor, maize yield are tilted by variation in the seasonal climate. In addition, we illustrate strategies that could allow farmers a way to manage the climate risk associated with these shifts. We illustrate maize-yield sensitivity to seasonal climate by simulating the influence of relatively small variations in dry spell duratio...

The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December–February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross–time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region.

The natural ecosystems of the Northeast Region of Brazil (NEB) have experienced persistent drought episodes and environmental degradation during the past two decades. In this study, we examined the spatial heterogeneity and temporal dynamics of the NEB using a 20-year time series of Normalized Difference Vegetation Index (NDVI) observations, derived from the National Oceanographic and Atmospheric Administration (NOAA)-Advanced Very High Resolution Radiometer (AVHRR) instrument. A set of 12 000 spatially distributed NDVI values was analysed to investigate significant deviations from the mean-monthly values of the base period (1982-2001) in the study area. Various statistical analyses involving minimum, mean and maximum values, coefficient of variation (CV), standardized anomalies (Z-scores), and 36-month running mean were applied to monthly NDVI values to identify spatial and temporal variations in vegetation dynamics. We found strong seasonal oscillations in the vegetation-growing season (February-May) over the NEB study area, with maximum NDVI observed in April-May and seasonal variations, expressed by the CV, ranging from 14% to 32%. In addition, a consistent upward trend in vegetation greenness occurred over the period 1984-1990, and was strongly reversed in the subsequent period 1991-1998. These upward and downward trends in vegetation greenness followed an inter-annual oscillation of ∼7-8 years. We also found that dry season peak (September) latitudinal variations in NDVI were 20-25% greater in 1991-1999 than 1982-1990 across the study region. The results of this study suggest that patterns in NEB vegetation variability were a result of the impact of enhanced aridity occurring over the last decade of the 20th century. © 2006 Elsevier Ltd. All rights reserved.

In Indonesia, drought driven fires occur typically during the warm phase of the El Nino Southern Oscillation. This was the case of the events of 1997 and 2015 that resulted in months-long hazardous atmospheric pollution levels in Equatorial Asia and record greenhouse gas emissions. Nonetheless, anomalously active fire seasons have also been observed in non-drought years. In this work, we investigated the impact of temperature on fires and found that when the July-October (JASO) period is anomalously dry, the sensitivity of fires to temperature is modest. In contrast, under normal-to-wet conditions, fire probability increases sharply when JASO is anomalously warm. This describes a regime in which an active fire season is not limited to drought years. Greater susceptibility to fires in response to a warmer environment finds support in the high evapotranspiration rates observed in normal-to-wet and warm conditions in Indonesia. We also find that fire probability in wet JASOs would be considerably less sensitive to temperature were not for the added effect of recent positive trends. Near-term regional climate projections reveal that, despite negligible changes in precipitation, a continuing warming trend will heighten fire probability over the next few decades especially in non-drought years. Mild fire seasons currently observed in association with wet conditions and cool temperatures will become rare events in Indonesia.

Fire is becoming a pervasive driver of environmental change in Amazonia and is expected to intensify, given projected reductions in precipitation and forest cover. Understanding of the influence of post-deforestation land cover change on fires in Amazonia is limited, even though fires in cleared lands constitute a threat for ecosystems, agriculture, and human health. We used MODIS satellite data to map burned areas annually between 2001 and 2010. We then combined these maps with land cover and climate information to understand the influence of land cover change in cleared lands and dry-season severity on fire occurrence and spread in a focus area in the Peruvian Amazon. Fire occurrence, quantified as the probability of burning of individual 232-m spatial resolution MODIS pixels, was modeled as a function of the area of land cover types within each pixel, drought severity, and distance to roads. Fire spread, quantified as the number of pixels burned in 3 × 3 pixel windows around each focal burned pixel, was modeled as a function of land cover configuration and area, dry-season severity, and distance to roads. We found that vegetation regrowth and oil palm expansion are significantly correlated with fire occurrence, but that the magnitude and sign of the correlation depend on drought severity, successional stage of regrowing vegetation, and oil palm age. Burning probability increased with the area of nondegraded pastures, fallow, and young oil palm and decreased with larger extents of degraded pastures, secondary forests, and adult oil palm plantations. Drought severity had the strongest influence on fire occurrence, overriding the effectiveness of secondary forests, but not of adult plantations, to reduce fire occurrence in severely dry years. Overall, irregular and scattered land cover patches reduced fire spread but irregular and dispersed fallows and secondary forests increased fire spread during dry years. Results underscore the importance of land cover management for reducing fire proliferation in this landscape. Incentives for promoting natural regeneration and perennial crops in cleared lands might help to reduce fire risk if those areas are protected against burning in early stages of development and during severely dry years.

Background: New sources of hydroclimate information based on forecast models and observational data have the potential to greatly improve the management of water resources in semi-arid regions prone to drought. Better management of climate-related risks and opportunities requires both new methods to develop forecasts of drought indicators and river flow, as well as better strategies to incorporate these forecasts into drought, river or reservoir management systems. In each case the existing institutional and policy context is key, making a collaborative approach involving stakeholders essential. Methods: This paper describes work done at the IRI over the past decade to develop statistical hydrologic forecast and water allocation models for the semi arid regions of NE Brazil (the “Nordeste”) and central northern Chile based on seasonal climate forecasts. Results: In both locations, downscaled precipitation forecasts based on lagged SST predictors or GCM precipitation forecasts exhibit quite high skill. Spring-summer melt flow in Chile is shown to be highly predictable based on estimates of previous winter precipitation, and moderately predictable up to 6 months in advance using climate forecasts. Retrospective streamflow forecasts here are quite effective in predicting reductions in water rights during dry years. For the multi-use Oros reservoir in NE Brazil, streamflow forecasts have the most potential to optimize water allocations during multi-year low-flow periods, while the potential is higher for smaller reservoirs, relative to demand. Conclusions: This work demonstrates the potential value of seasonal climate forecasting as an integral part of drought early warning and for water allocation decision support systems in semi-arid regions. As human demands for water rise over time this potential is certain to rise in the future.

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

CERES-Wheat, a dynamic process crop growth model, is specified and validated for eight sites in the major wheat-growing regions of China. Crop model results are then used to test the Mitscherlich-Baule and the quadratic functional forms for yield response to nitrogen fertilizer, irrigation water, temperature, and precipitation. The resulting functions are designed to be used in a linked biophysical-economic model of land-use and land-cover change in China. While both functions predict yield responses adequately, the Mitscherlich-Baule function is preferable to the quadratic function because its parameters are biologically and physically realistic. Variables explaining a significant proportion of simulated yield variance are nitrogen, irrigation water, and precipitation; temperature was a less significant component of yield variation within the range of observed year-to-year variability at the study sites. Crop model simulations with a generic soil with median characteristics of the eight sites compared to simulations with site-specific soils showed that agricultural soils in China have similar and, in general, low-to-moderate water-holding capacities and organic matter contents. The validated crop model is useful for simulating the range of conditions under which wheat is grown in China, and provides the means to estimate production functions when experimental field data are not available.

Agricultural monitoring is a major concern to economies largely based on agriculture like Uruguay. In order to improve crop yield forecasts, identification of crop types must be performed early in the planting season. However, this task is generally difficult because of the spatial heterogeneity of the landscape, the different crop cycles, the spectral similarity with grassland, and the inter-annual variability due to climatic events and fallow periods. In collaboration with INIA, this study investigates remote sensing methods for dynamic mapping of cropland areas and for producing a map of winter and summer crops at 250m using MODIS time series. The originality of the approach consists of: (i) exploiting all the multi-spectral information using an adaptive compositing method for a better discrimination of cropland types and to better capture their spatio-temporal variability; (ii) a spatio-temporal analysis of various land use types prior to the classification for a better knowledge of crops behaviours and the selection of the most discriminating seasons in the classification; and (iii) combining NDVI profiles, multi-spectral composites with reference dataset, high resolution images and expert knowledge. The accuracy of the product is assessed based on a reference dataset of crop fields. The results confirm the relevance of MODIS time series in term of spatial and temporal resolutions for mapping cropland areas and characterizing the inter-annual variability. Thanks to a good reference dataset and an analysis of crops spectro-temporal behaviour, it was possible to identify cropland areas from other land use types and discriminate summer crops from winter crops., JRC.H.4-Monitoring Agricultural Resources