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The increasingly inter-connected global food system is becoming more vulnerable to production shocks owing to increasing global mean temperatures and more frequent climate extremes. Little is known, however, about the actual risks of multiple breadbasket failure due to extreme weather events. Motivated by the Paris Climate Agreement, this paper quantifies spatial risks to global agriculture in 1.5 and 2 °C warmer worlds. This paper focuses on climate risks posed to three major crops - wheat, soybean and maize - in five major global food producing areas. Climate data from the atmosphere-only HadAM3P model as part of the "Half a degree Additional warming, Prognosis and Projected Impacts" (HAPPI) experiment are used to analyse the risks of climatic extreme events. Using the copula methodology, the risks of simultaneous crop failure in multiple breadbaskets are investigated. Projected losses do not scale linearly with global warming increases between 1.5 and 2 °C Global Mean Temperature (GMT). In general, whilst the differences in yield at 1.5 versus 2 °C are significant they are not as large as the difference between 1.5 °C and the historical baseline which corresponds to 0.85 °C above pre-industrial GMT. Risks of simultaneous crop failure, however, do increase disproportionately between 1.5 and 2 °C, so surpassing the 1.5 °C threshold will represent a threat to global food security. For maize, risks of multiple breadbasket failures increase the most, from 6% to 40% at 1.5 to 54% at 2 °C warming. In relative terms, the highest simultaneous climate risk increase between the two warming scenarios was found for wheat (40%), followed by maize (35%) and soybean (23%). Looking at the impacts on agricultural production, we show that limiting global warming to 1.5 °C would avoid production losses of up to 2753 million (161,000, 265,000) tonnes maize (wheat, soybean) in the global breadbaskets and would reduce the risk of simultaneous crop failure by 26%, 28% and 19% respectively. • Climate data from the atmosphere-only HadAM3P model were used to estimate the risks of climatic extreme events in the global breadbaskets. • To analyse the risks of simultaneous breadbasket failure, the copula methodology was applied. • Projected wheat, maize and soybean yield losses in the global breadbaskets increase disproportionately between 1.5 and 2 °C global warming. • The highest simultaneous climate risk increase in the breadbaskets between the two warming scenarios was found for wheat, followed by maize and soybean. [ABSTRACT FROM AUTHOR]

Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. The literature search produced 11,908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. [Display omitted] • Recent advances in models and data used to predict crop yield were examined to identify any patterns and trends. • Statistical models were the most common but there was a surge in the use of machine learning models after 2018. • Quantitative analytical techniques were used to identify factors that affected the model's predictive ability. • Machine learning models were more accurate than process-based models but not statistical models. • Combining agro-meteorological with remotely sensed data was more accurate than using agro-meteorological data only. [ABSTRACT FROM AUTHOR]

Abstract The paper investigates the impact of rainfall shocks on agricultural productivity and crop-specific agricultural land productivity. The paper also examines the impact of negative rainfall shocks on household consumption as well as its distributional impact by initial wealth and geographical zones. We use nationally representative panel datasets from Nigeria merged with georeferenced rainfall information. Negative rainfall shocks have heterogeneous effects on crop-specific agricultural productivity and based on geographical zones. We use an instrumental variables regression approach, where agricultural land productivity is instrumented with negative rainfall shocks. A negative rainfall shock decreases agricultural productivity and hence decreases household consumption by 37%. We also show considerable differential impacts of rainfall shocks on household consumption by initial values of wealth and geographical zones. Rainfall shocks have a negative, significant impact for asset-poor and nonpoor households, but has a higher impact on household consumption for asset-poor households. Similarly, it has higher impact for land-poor households and households in northern Nigeria. Highlights • The impact of rainfall shocks on crop-specific agricultural productivity was investigated. • The paper examines the impact of negative rainfall shocks on household consumption. • The distributional impact of rainfall shocks was examined. • Heterogeneous effects of rainfall shocks were identified. • Differential impacts of rainfall shocks on household consumption was observed. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive substance flow analysis (SFA) of phosphorus (P) for a six-year period (2008–13) in Gippsland, a key agricultural region of Australia with high economic and environmental significance. The analysis has revealed that around 71% (10,904 t) of the mean annual total inflow of P was stored in this region. This finding is different from other published regional scale SFAs, where more than 50% of the P in annual total inflow eventually left the region. Per capita P inflow in Gippsland is also found substantially higher compared to available SFAs. In Gippsland, the annual inflow of P primarily occurred as commercial fertilizer (66% or 10,263 t) and livestock feed (29% or 4443 t), and the outflow mainly occurred as livestock products (94% or 4181 t); while the majority (66% or 7218 t) of the P storage occurred in soils of the livestock farming area. A comparative analysis of the magnitude of P flow in different subsystems indicates that more than 80% of the annual total inflow, outflow, and storage of P in this region is associated with the livestock (mainly dairy and meat cattle) farming subsystem. For the Gippsland region as a whole and almost all subsystems, significant annual variations in the magnitudes of P inflow, outflow, storage and internal flow have been observed. Between 2008 and 2013, both the annual total inflow (mainly as commercial fertilizer) and the annual total storage (mainly in soils) of P in this region showed a substantial decrease (41% and 54% of the 2008 level, respectively), while the annual total outflow (mainly as livestock and crop products) remained nearly the same, indicating an improvement towards sustainable P management. Despite such a positive sign, there is still adequate room for improvement. This analysis indicates that over the study period, about 65,424 t P were accumulated (mainly as soil storage) that is approximately six times the mean annual P inflow as commercial fertilizer in this region; while approximately 3241 t P lost as soil erosion and runoff, indicating substantial adverse economic and environmental implications. Based on the findings of the current analysis, this paper outlines a wide range of policy and management interventions to reduce the downstream loss of P and other nutrients as well as the region's dependency on imported commercial fertilizers and grain based feed. This paper also presents new criteria for data quality analysis and a set of P concentration data of various materials that could be readily utilized in future SFAs of P at any geographical scale. This paper suggests that considering the inter-annual variations in P flow as assessed in this SFA, future research should focus on identifying the influence of socio-environmental, technological and political factors on the magnitude P flow in Gippsland. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential input for food production, but at the same time N raises serious concerns for air, water and soil pollution due to its overuse. Research shows that N use is influenced by socio-economic factors beyond the environmental circumstances while heavily depending on the agronomic practices and technologies used. Micro-level studies based on real farm observations are still scarce in this context. Therefore, this study estimated N use efficiency (NUE) and its climatic and socio-economic determinants, as well as N 2 O and NH 3 emissions from organic and inorganic fertiliser-amended fields. Through exploring primary data collected from 155 wheat growers from north-western Bangladesh, we estimated NUE of wheat production as the ratio of the total quantity of N produced in the harvested grain and the total quantity of N applied to soil through inorganic and organic fertilisers. Then we estimate a Tobit model to determine the socio-economic and farm-level practices influencing NUE. Finally, for N 2 O and NH 3 emissions, we applied region-specific emission factors, which are not sensitive to management practices but can give an approximation of emission and emission intensity of the production. The estimated mean NUE for North-West Bangladesh was 37.4%, which widely varied across different farm categories and farming practices used. Agronomic practices, including those induced by climate change, positively contributed to NUE. NUE was higher for the farmers cultivating high-yielding, drought-tolerant, and short-duration wheat varieties. NUE significantly increased when a farmer applied a combination of organic and inorganic fertilisers. Irrigation water pricing was critical in NUE since it influences farmers' water management practices. A notable quantity of N loss as N 2 O (5.98 kg/ha) and NH 3 (45.58 kg/ha) was emitted, and the farms managed by landless and marginal farmers emitted quantities more than others. Farmers with better landholding size, education and access to extension services operated at a relatively higher level of NUE. The policy suggestions to increase NUE include strengthening extension services, particularly for promoting a balanced supply of nutrients from organic and inorganic fertilisers, promoting volumetric irrigation water pricing and encouraging long-term tenurial arrangement. The paper is relevant to policy makers since, unlike earlier works based on experimental data, it estimates more robust indices of NUE and considers the role of different farms and farmer-specific socio-economic, anthropogenic and institutional factors in farm production. Finally, the paper estimates N 2 O and NH 3 emissions associated with wheat production. [Display omitted] • Low nitrogen use efficiency (NUE) is a concern in crop production in Bangladesh, along with many countries globally. • Analysing farm survey data, the paper estimated NUE, gaseous emissions inferred based on presumed defaults. • The estimated mean NUE was 37.4% which was found to be related to several socioeconomic factors. • Farm data, as opposed to experimental data, allowed assessing the effects of variables on NUE that would be hard otherwise. • The paper is novel in finding agronomic, socioeconomic and environmental determinants of NUE including climate change. [ABSTRACT FROM AUTHOR]

Since the 1980s, many developing countries have introduced policies to promote seed industry growth and improve the delivery of modern science to farmers, often with a long-term goal of increasing agricultural productivity in smallholder farming systems. Public, private, and civil society actors involved in shaping policy designs have, in turn, developed competing narratives around how best to build an innovative and sustainable seed system, each with varying goals, values, and levels of influence. Efforts to strike a balance between these narratives have often played out in passionate discourses surrounding seed rules and regulations. As a result, however, policymakers in many countries have expressed impatience with the slow progress on enhancing the contribution of a modern seed industry to the overarching goal of increasing agricultural productivity growth. One reason for this slow progress may be that policymakers are insufficiently cognizant of the trade-offs associated with rules and regulations required to effectively govern a modern seed industry. This suggests the need for new data and analysis to improve the understanding of how seed systems function. This paper explores these issues in the context of Asia's rapidly growing seed industry, with illustrations from seed markets for maize and several other crops, to highlight current gaps in the metrics used to analyze performance, competition, and innovation. The paper provides a finite set of indicators to inform policymaking on seed system design and monitoring, and explores how these indicators can be used to inform current policy debates in the region. [ABSTRACT FROM AUTHOR]

Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms. The result showed that 33 variables measured by various sensors were demonstrated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled environment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation. Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems. [Display omitted] • We searched and reviewed the most relevant 115 papers on Ag-IoT published between 2011 and 2021. • The papers were analyzed focusing on sensors, actuators, main boards, crops, communication protocols, and power supplies. • Ag-IoT components, challenges, potential solutions, and supporting technologies were presented and discussed. • The benefits of Ag-IoT for farming systems analyses and management were discussed. • We concluded with the future direction of designing an Ag-IoT system with completeness, robustness, and compatibility. [ABSTRACT FROM AUTHOR]

Pesticide-free but non-organic production systems have been recently introduced in public and private programs in Europe. However, a key challenge is to define what 'pesticide-free' comprises and means. This paper aims to give insights in and critical reflections on the definition and regulation of pesticide-free crop production systems in real-world programs. To this end, we review four examples of public and private pesticide-free production programs in European crop production (i.e. from France, Germany and Switzerland). Our analysis reveals several semantic and regulatory inconsistencies and potential confusion that exists around the term pesticide-free crop production, i.e. we find that it is far from clear-cut what pesticide-free production systems are. For example, programs differ with respect to which part of the crop management cycle is regulated. Moreover, all programs offer various exceptions for pesticide use. Definitions and regulations differ widely across case studies. For example, to participate in governmental programs on pesticide-free production farmers in one country have different requirements than in another. Moreover, governmental regulations not match with those in private programs and vice versa. Pesticide-free production programs can be a useful and viable addition to European agricultural and food systems. However, based on the here presented insights, we formulate eight recommendations for policy and industry how to clarify, align and harmonize pesticide-free crop production. [Display omitted] • Pesticide free but non-organic production is of increasing relevance in European agriculture. • We investigate the design of real-world examples of governmental and private pesticide free production programs. • Definitions of pesticide free production differ widely and are often inconsistent. • We draw conclusions for policy and industry how to clarify and harmonize pesticide free production programs. [ABSTRACT FROM AUTHOR]

Agricultural food production is both affected by and contributing to climate change. At the global scale, agri-food systems are responsible for one-third of total greenhouse gas emissions. With progressing climate change, the risks of crop failure increase. Thus, an urgent need is to reduce emissions from food systems while increasing their resilience to climate change. Enormous untapped potentials to achieve these dual goals lie in transforming agri-food systems towards more diverse, plant-based, and regional food production systems. In this paper, we present an innovative approach for identifying climate-adapted alternative food crops that could (1) help to diversify existing cropping systems and thus increase their climate resilience and can be (2) nutritious elements of plant-based regional diets with reduced emissions. The approach builds on the model ecocrop to select food crops that could benefit from regionally projected changes in climate. The model-based analysis is complemented with a literature review to examine the ecocrop results for their plausibility and provide a broader assessment of potentials for cultivation, utilization, and nutritional values of model-selected crops. The approach is applied to Switzerland, where we identify eight alternative crops with the potential to increase climate resilience while contributing to healthy human diets of regional consumers with benefits for climate mitigation (almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage). The literature review indicated that the increasing demand for many of these crops suggests great potential for regional marketing of crop products. The results produced in this study provide an initial guide for researchers and innovative farmers interested in experimenting with alternative crops in Switzerland, thus promoting climate-smart food system transformation from the production side. Using our unbiased bottom-up screening approach, we identified climate-adapted alternative crops that can provide essential nutrients, cover nutritional gaps in Switzerland, diversify existing production systems, and improve sustainability. [Display omitted] • Innovative bottom-up screening approach identifies climate-adapted alternative crops. • Almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage are identified. • Identified crops provide essential nutrients and cover nutritional gaps in Switzerland. • Selected crops can diversify existing production systems to improve sustainability. [ABSTRACT FROM AUTHOR]

Rainfed transplanted rice ( Oryza sativa ) is the staple crop of the East India Plateau, with >80% grown in a rice-fallow on terraced and bunded hill-slopes (‘medium-upland’) where it is low yielding and drought-prone despite high rainfall (>1200 mm). Paper I attributed this to inadequate ponding for transplanted rice whilst identifying the potential for risk-free alternative kharif (monsoon period) crops, including direct-seeded rice grown without ponding (‘aerobic’ rice), and for second-cropping with little or no irrigation. Paper II reports research with Tribal smallholders in Purulia District, West Bengal that aimed to evaluate these cropping options using a participatory process that further aimed to ‘improve the situation’ of participating families. The feasibility of short-duration aerobic rice was confirmed experimentally in 2007 and 2008 and in wider adoption by farmers in 2010 when conventional rice could not be transplanted. Best yields in each year were >4 t ha −1 . Mustard ( Brassica juncea ) and wheat ( Triticum aestivum ) planted after medium-duration rice yielded up to 0.95 t ha −1 and 2.6 t ha −1 with one irrigation of 40–50 mm for establishment; but modelling suggests there is enough residual soil water after short-duration (early-maturing) rice to exceed these yields in most years, even without irrigation. Significant P-fertiliser was required with these crops to correct acute deficiency. Rainfed vegetables were grown in the kharif and then adapted by farmers to pre- kharif cropping, and to the rabi (winter) if they had some access to irrigation. Monitoring land-use revealed rapid, sustained adoption of more diverse and intensive cropping, with significant social and economic benefits. We attributed adoption to the participatory process used, that strengthened farmer's capacity to innovate. The systems implemented by farmers needed no expenditure on new water resources, suggesting that comprehensive watershed development (WSD) is not a prerequisite to replacing the rigid rice-fallow with safer climate-responsive systems, although investment in small water harvesting structures may be needed for rabi vegetable crops. The technology evaluated, plus the process of intervention that built capacity, together provide a foundation for wider adoption of less risky cropping systems with greater water productivity. [ABSTRACT FROM AUTHOR]

In complex mixed crop-livestock systems with limited resources and biomass scarcity, crop residues play an important but increasingly contested role. This paper focuses on farming systems in the semi-arid areas of Zimbabwe, where biomass production is limited and farmers integrate crop and livestock activities. Conservation Agriculture (CA) is promoted to intensify crop production, emphasizing the retention of surface mulch with crop residues (CR). This paper quantifies the associated potential economic trade-offs and profitability of using residues for soil amendment or as livestock feed, and explores alternative biomass production options. We draw on household surveys, stakeholder feedback, crop, livestock and economic modeling tools. We use the Trade-Off Analysis Model for Multi Dimensional Impact Assessment (TOA-MD) to compare different CR use scenarios at community level and for different farm types: particularly the current base system (cattle grazing of maize residues) and sustainable intensification alternatives based on a CA option (mulching using maize residues ± inorganic fertilizer) and a maize–mucuna ( Mucuna pruriens ) rotation. Our results indicate that a maize–mucuna rotation can reduce trade-offs between CR uses for feed and mulch, providing locally available organic soil enhancement, supplementary feed and a potential source of income. Conservation Agriculture without fertilizer application and at non-subsidized fertilizer prices is not financially viable; whereas with subsidized fertilizer it can benefit half the farm population. The poverty effects of all considered alternative biomass options are however limited; they do not raise income sufficiently to lift farmers out of poverty. Further research is needed to establish the competitiveness of alternative biomass enhancing technologies and the socio-economic processes that can facilitate sustainable intensification of mixed crop-livestock systems, particularly in semi-arid environments. [ABSTRACT FROM AUTHOR]

Improvements in agricultural water productivity with constrained water resources are often regarded a prerequisite to meet food demands of a growing world population. The WaPOR data portal was launched to monitor biomass, evapotranspiration and biomass water productivity in Africa and the Near East using remote sensing technologies. The WaPOR database shows spatial pixel variation in biomass, suggesting scope to improve water productivity at field level. The aim of this paper is to assess with regression analyses for different spatial and temporal scales whether spatial variability in biomass and evapotranspiration as revealed by WaPOR can be attributed to human influenceable factors, variations in local climate, or methodologically inherent inacuracies of the WaPOR data. Variation in biomass and evapotranspiration data was assessed through agronomic linear regression analyses, for two large-scale irrigated sugarcane estates in Ethiopia (Wonji) and Mozambique (Xinavane). In these cases 82–94% of the variation in biomass and evapotranspiration is attributed to crop photosynthetic efficiency (very large influence), local climate (large influence) and irrigation technology (small influence). The remaining unexplained spatial variability is small (6–18%) and falls within an error range of +/− 9%. In conclusion, WaPOR performed very well by neatly reproducing the conservative relationship between biomass and evapotranspiration, which also means there is very limited scope to improve biomass water productivity through WaPOR monitoring. Further research is recommended on the magnitude of WaPOR accuracy and other sources that explain variations in biomass and evapotranspiration. Applicability of the WaPOR database to monitor biomass water productivity was assessed. Spatial variability in biomass and evapotranspiration data largely stemmed from photosynthesis and local climate, factors farmers and water managers can hardly influence. [Display omitted] • WaPOR remote sensing database monitors biomass (B) and evapotranspiration (ET). • Variation in WaPOR data for B and ET was assessed through agronomic analyses. • Nearly all variation was attributed to crop, local climate and irrigation method. • Remaining (unexplained) variation falls within an accuracy range of +/− 9%. • Scope to improve biomass water productivity through WaPOR monitoring is very small. [ABSTRACT FROM AUTHOR]

Indian agriculture is highly vulnerable to climate shocks, such as floods, droughts and heat-stress. In this paper, using a dynamic panel-data approach we have assessed the impact of rainfall-deficit and heat-stress on agricultural productivity, and subsequently evaluated effectiveness of crop diversification in mitigating their adverse effects. The findings show that both rainfall-deficit and heat-stress damage agricultural productivity, and the damage increases with increase in their severity. Nevertheless, we find crop diversification as an important ex ante adaptation measure to climatic shocks and its adaptation benefits are more apparent against severe shocks and in the long-run. Our findings reinforce the dynamic role of crop diversification in improving resilience of agricultural production systems to climatic shocks. • There exists a dynamic relationship between climatic shocks, diversification and system productivity. • Climatic shocks adversely affect system productivity, and the effect intensifies with their rising severity. • Diversification enhances resilience of production system to climatic shocks. • Resilience benefits of diversification are more apparent in the long-run. [ABSTRACT FROM AUTHOR]

The Peruvian Amazon is undergoing rapid and uneven economic growth, alongside alarming rates of deforestation, increasing land use change and food security concerns. Although it has been widely acknowledged that food insecurity is intrinsically linked with deforestation, the links have not been thoroughly documented. The aim of this paper is to analyse the trade-offs and synergies between food security and forest exploitation at household level in mestizo communities in Ucayali, one of the regions with the highest deforestation rates in the Peruvian Amazon. To this end, 24 farmers were interviewed, surveys were conducted with a sample of 58 households, and an ad-hoc simulation modelling tool was developed and applied. Four main types of mestizo farming households were identified based on their crop and livestock diversity. For all farm types, the forest mainly represented a set aside area to support a potential increase in agricultural production. However, simulations showed that the different types of households, with different decision rules, lead to different rates of deforestation. The results of this study showed that the most diversified farming households presented the smallest trade-offs between food security and forest conservation, as they are the ones most likely to preserve the forest while ensuring their food security. • Whole farm simulation tools allow assessing the links between food security and forest exploitation. • In the mestizo communities of Ucayali gathering of forest fruits is minimal. • Farm diversification can support synergies between forest conservation and food security. [ABSTRACT FROM AUTHOR]

Abstract Each month the JRC issues the MARS Bulletin detailing the agro-meteorological and expert analysis underpinning the assessment of European crops' status and yield forecasts. In this context a resume is provided to give an overview on the geographical distribution of eventual crop damages. The MARS Bulletin provides such information in a set of synthetic maps (Areas of Concern), produced in each Bulletin, depicting extreme weather events and their impact on crops that have occurred in Europe during the analysis period. The present article describes the mix of quantitative and qualitative datasets and methodologies that drive the delineation of the Areas of Concern (AOC) maps and evaluates their capability to resemble crop production losses. The quantitative analysis is based on the Mars Crop Yield Forecasting System (MCYFS) indicators coming from meteorological models, crop growth models and remote sensing data. Indicators are considered in absolute and relative terms and in their relation with standard statistical metrics. The outcome of the quantitative analysis is a set of potential Areas of Concern. Experts' judgment is thus necessary to discriminate potential results through a qualitative analysis focused on: past occurred events and climatologic conditions; agro-management practices; regional agricultural systems peculiarity and their historical resilience and resistance to adverse conditions. In this article the experts' judgment of the team of current MARS analysts, as used in the AOC analysis, is translated into a warning index. Such index condenses the specific contribution to the final production of each development stage and the adverse agrometeorological events occurred, as depicted into the AOC maps. The warning index is computed at country scale for the past five agricultural seasons, from season 2011-2012 to season 2015-2016. Two crops are considered, winter wheat and grain maize as proxy for winter and summer crop groups. The warning indexes calculated are then compared to the national production in a qualitative way. To support the analysis few study cases are presented. The findings of this article highlight that the events depicted in the AOC maps are informative about production losses and specific knowledge is needed to full understand the information carried. Highlights • The comparison of the issued warnings through AOC analysis shows some good agreements with production anomalies. • AOC analysis properly assessed most of the winter wheat production anomalies. • AOC analysis could provide warning relevant at regional scale only. • AOC analysis tends to underestimate unfavorable conditions at harvest. • This paper represents the first attempt to formalize the tacit knowledge within the group of MARS analysts. [ABSTRACT FROM AUTHOR]

Abstract Meeting future food demands for 9 billion people in the next 30 years will require either agricultural expansion or intensification to increase production. However, agriculture is already a major driver of biodiversity loss, as well as freshwater withdrawals, nutrient inputs, and greenhouse gasses, among other pressing environmental issues. In this paper, we look for solutions to this production-conservation challenge at the subfield scale. We use precision agriculture yield data from three farms in Southern Ontario and convert them into "profit maps" that show which regions of a field have management costs that exceed the market value of the commodities produced. We analyse the profit of three farms over time and identify areas that consistently show low or negative profit and thus constitute a compelling case for taking these areas out of production. We find, for example, that up to 14% of farmland can result in money loss and even more than 50% of the land might still not meet minimum revenue expectations. Further, we assess the economic feasibility of conservation strategies on these set-aside lands and find that investing in environmental benefits (even minimally) can often times be inexpensive when compared with economic losses due to failed harvests. We argue that profit mapping can serve as a management tool for farmers that will allow them to identify optimal crop areas, optimize nutrient inputs, plan for ecological intensification, and avoid economic loss all while providing ecosystem services at the local scale. Highlights • Precision agriculture yield data are converted into profit maps. • Profit maps let us identify set-aside areas that can be taken out of production. • Conservation in set-aside lands can be more economically feasible than agriculture. • This methodology can help farmers manage risk and contribute to ecosystem services. [ABSTRACT FROM AUTHOR]

Abstract Global population growth will require substantial increases in agricultural production worldwide. Yet, despite growing concern about the environmental and social impacts of increased agricultural productivity, no consensus exists on the appropriate method for assessing the appropriate tradeoffs for sustainability. To address this need, this paper proposes the use of Data Envelope Analysis to create an index that permits assessment of the relative sustainability of smallholder farms in a given region, with minimal external interpretation about how individual farmers weight tradeoffs on their own farms. The method is applied to the Ethiopian highlands to explore the determinants of economic, social and environmental sustainability in the region's agricultural sector. Econometric model results suggest that farmers felt that farm size, market access, access to off farm income, agricultural loans, and access to agricultural extension and demonstration plots are key drivers of agricultural sustainability at the farm-level. Differences in agro-ecological conditions and region-specific factors were also significant determinants of relative farm sustainability. This underscores the importance of geographical targeting and tailoring of interventions to increase farm sustainability. Highlights • Sustainability indicators are context-specific and require knowledge of local farming systems • Data Envelopment Analysis used to derive endogenous weights for aggregating 15 indicators of agricultural sustainability • Larger farms exhibited higher scores of relatively farm sustainability • Geographical targeting important for sustainable intensification of smallholder systems [ABSTRACT FROM AUTHOR]

Soil water is important for agricultural production, and is a key parameter in hydrologic models and weather prediction models. In this paper we explore the ability of two commonly used paddock scale models for pasture: HowLeaky and DairyMod, to estimate soil water in the topsoil (0–100 mm) and root-zone (0–1000 mm) at daily intervals. We also examined the influence of soil hydraulic properties on estimated amounts of soil water by using soil properties measured at the site, compared with properties from the national database. Soil water estimates were compared with field measurements from four grazing systems in the high-rainfall zone of western Victoria, Australia. Daily soil water amounts in the topsoil and root-zone were well simulated by both Howleaky and DairyMod, with Howleaky performing better overall. Soil water was simulated more accurately for the root-zone than for the topsoil. On average, the error in root-zone water estimations with site-derived soil properties was 18% of plant available water capacity, and ranged from 8 to 24% of their plant available water. When soil properties were instead taken from the national database, the actual values of soil water were predicted poorly, with an average bias of 86 mm. However, relative soil water is the parameter of greater interest, and normalising by the waterholding capacity corrected these biases leading to an average error of 24% of the plant available capacity, with a range across sites from 8 to 31% of the plant available capacity. Nevertheless, with site-derived parameters bias was reduced by more than half in all sites. Errors in model predictions tended to increase during the growing season reaching a maximum at the most critical times of the year for tactical decision-making by farmers (November and December). Superior performance of Howleaky in estimating soil water and other water balance components support the application of Howleaky for soil water simulations in pasture sites at point scale. Improving algorithms for soil water redistribution would be beneficial to increase the model performance. [ABSTRACT FROM AUTHOR]

This paper shows how crop losses, display Self-Organized Critical Behavior, which implies that under a wide range of circumstances, these losses exhibit a power-law dependence on frequency in the affected area whose order of magnitude approximates those reported for extreme climate events. Self-Organized Critical Behavior has been observed in many extreme climate events, as well as in the density and distribution of pests linked to crop production. Empirical proof is provided by showing that the frequency-size distribution of the cropland loss fits the Pareto and the Weibull models with scaling exponents that are statistically similar to the expected value. In addition, the test included comparisons of the expected value and the predicted value of the scaling exponents among different subsystems and among systems of the same universality class. Results show that the Pareto model fits the heavy-tailed distribution of losses mostly caused by extreme climate events, while the Weibull model fits the whole distribution, including small events. The analyses show that crop losses adopt Self-Organized Critical Behavior regardless of the growing season and the water provision method (irrigated or rainfed). Irrigated systems show more stable behavior than rainfed systems, which display higher variability. The estimation is robust not only for calculating model parameters but also for testing the proximity to a power-law-like relationship. A long-term risk index by growing season and water provision method is derived as an application of this power-law behavior. The index is flexible, comparable between geographical units regardless of their size and provides an indirect measure of the probability of losing a cropping area of a given size. [ABSTRACT FROM AUTHOR]

Differences in land tenure regimes are one challenge to implementing soil conservation practices in agricultural systems. It is frequently assumed that tenants are less likely to adopt soil conservation strategies than owners, given a shorter-term engagement with the field. Also, the field's environmental potential (i.e., potential for agricultural production) may influence farmers' investment decisions, since high-potential fields increase the chances of achieving a return on the investment. Understand the effect of land tenure regimes and environmental potential on fertilization rates and balance of nitrogen, phosphorus, and sulfur in soybean and maize crops in Argentina. We applied mixed-effects models on a database of 52,588 fields of soybean and maize farms, covering a total area of 3.8 M ha in Argentina during the period of 2017–2022. Overall, the balance of nitrogen, phosphorus, and sulfur was (mean ± SE) −29.11 ± 0.15, −2.58 ± 0.38, and 8.26 ± 0.044 kg ha−1 yr−1, respectively. Despite 8.04 and 0.63 kg ha−1 yr−1 more nitrogen and phosphorus were applied in high-potential compared to low-potential maize fields, nutrient outputs were still higher. Therefore, net nutrient exports of the most productive fields increased by 9.99 and 2.06 kg ha−1 yr−1 for nitrogen and phosphorus, respectively. In soybean fields, environmental potential had no effect on nutrient application, but the net export of nitrogen and phosphorus was 9.85 and 2.14 kg ha−1 yr−1 higher in high-potential fields compared to low-potential fields. Tenure regime had a weak effect, mainly on phosphorus. On average, owners applied 0.37 kg ha−1 yr−1 more and exported 0.28 kg ha−1 yr−1 less phosphorus than tenants in both crops. Sulfur application and balance were weakly affected by the studied variables, and the positive balance suggests overfertilization under the assumptions of this paper. We conclude that the Argentine farming system depletes some of the main nutrients, regardless of the field's environmental potential or the land tenure system. The effect of the tenure regime is overwhelmed by the impact of environmental potential on farmers' fertilization management, with high-potential fields degrading due to soil mining at a faster pace than low-potential fields, putting future yields at risk. By exploring a farming system based on nutrient depletion, our results contribute to the general understanding of tenure regime consequences on soil degradation. Argentinean farmers should consider increasing N and P application and contemplate environmental heterogeneity to avoid nutrient mining and degradation of one of the most productive areas of the world. [Display omitted] • The influence of land tenure and environmental potential on fertilization and nutrient balance was studied. • Net nutrient exportation prevailed regardless of tenure regime and environmental potential, except for sulfur. • Land tenure had a low effect on fertilization and nutrient balance. • In fields of high environmental potential, more nutrients were depleted than in fields of low environmental potential. • Environmental heterogeneity should be considered when fertilizing. [ABSTRACT FROM AUTHOR]

The increasing and often conflicting challenges that agricultural production systems face today require a more comprehensive approach to planning in the sector, integrating the economic principles of production with its social characteristics and ecological impacts. The paper presents an innovative attempt to incorporate biophysical criteria into a standard socio-economic optimisation model, illustrated through a study of the Slovenian dairy sector. The biophysical perspective on the system's functioning is determined by means of emergy analysis. This is an environmental accounting approach which reflects the cumulative environmental support needed to produce a certain output. The eco-centric perspective on the emergy approach complements the standard socioeconomic perspective of value that reflects the utility of a product (anthropocentric perspective). The model is developed based on a preceding analysis of socio-economic and emergy-based performance characteristics of different production types at the farm level that, when aggregated, constitute the sector. The multi-criteria optimisation model is supported by weighted goal programming (WGP) and aims to investigate the effects of two opposing agricultural policy paradigms on the organisation of the sector at the national level. The results show that a protectionist or eco-social focus of public interventions results in the sector's organisation with rather contrasting performance characteristics. The model outcome that represents a compromise between the two agro-political paradigms clearly suggests that incorporation of the emergy criterion into the optimisation model leads to a diverse and balanced structure and a more favourable economic and biophysical performance of the sector. Accordingly, the results confirm the complementarity of economic and emergy approaches and provide implications for a more comprehensive planning of agricultural activity. [ABSTRACT FROM AUTHOR]

This paper provides an ex-ante assessment of the impacts of climate and price variability on household income and food security in Ethiopia and Ghana. The study applies an agent-based modelling approach to highlight the role of coping and adaptation strategies under climate and price variability. Our simulation results show that climate and price variability adversely affects income and food security of households in both countries. Self-coping mechanisms are found to be important but insufficient to mitigate the adverse effects of variability, implying the need for policy interventions. Adaptation strategies composed of a portfolio of actions such as the provision of production credit and access to improved seeds are found to be effective in reducing the impacts of climate and price variability in Ethiopia. Similarly, policy interventions aimed at improving the provision of short-term production credit along with the existing irrigation facilities are important in Ghana. Finally, this study highlights the importance of capturing the distributional aspects of adaptation options by highlighting heterogeneous effects of variability and adaptation options. [ABSTRACT FROM AUTHOR]

Agricultural production in Northern Ghana is dominated by smallholder farm systems, which are characterized by low inputs and low outputs, declining soil fertility, large yield gaps and limited adoption of agricultural technologies. There is an urgent need for alternative farm designs that are more productive, yet more sustainable. Technology packages for sustainable intensification are promoted by an R4D project in the Upper East, Upper West and Northern Regions of Ghana. In this paper, we analyse differences in perceived suitability, and modelled technical impact per technology package. We used a locally validated framework to categorise farm systems diversity that considers both, the horizontal (between households) and vertical (within households) dimension of diversity. Farm households were classified along a gradient of resource endowment. We selected one representative farm per type and per region to assess and compare their socio-economic and environmental performance (farm profitability, labour and soil organic matter inputs) using the whole-farm model Farm DESIGN. We then used Farm DESIGN to assess the potential impact of five proposed technology packages and to explore promising alternative farm configurations. We discussed model assumptions and results with farmers, including alternative cropping patterns and trade-offs. We evaluated the packages with different household members using a weighted scoring technique, subsequently juxtaposing model results with farmer perceptions. Large differences prevailed among and within farms per type and per region, with low resource endowed farms being projected to benefit most in relative and least in absolute terms from an adoption of the packages. Farmer feedback confirmed the accuracy of alternative farm configurations, as determined by the model. However, the feedback also revealed that the most profitable farm designs would be hard to attain in reality, particularly for members of low and medium resource endowed households, due to high initial investment costs. Within households, women were more positive about the packages than men, since men heavily penalized extra costs and labour, translating into a greater congruence of model results with the male evaluation. We discuss the importance of distinguishing between technical (technology i.e. purchased tools and inputs) and managerial (techniques e.g. row planting) package components. We conclude that operationalizing inter- and intra-household diversity is a fundamental step in identifying sensible solutions for the challenges smallholder farm systems face in Northern Ghana. [ABSTRACT FROM AUTHOR]

Fusarium Head Blight (FHB) is an important problem to the agricultural production and marketing system, and has led to major economic losses for wheat and barley producers in the United States, Canada and many other countries. Deoxynivalenol (DON) is a mycotoxin associated with FHB. Grain products and feed grain contaminated with DON (commonly known as vomitoxin) are subject to FDA advisory limits and as a result end-users place restrictions on this factor. This has led to steep price discounts, as well as higher risks for producers and grain merchandisers. Research has led to development of varieties that are resistant to moderately resistant to FHB. Also, studies indicate combinations of genetic resistance, fungicides and some management practices (combine settings, tillage practices, etc.) can be used to decrease losses due to FHB. This increases cost to the industry and imputes a value related to reduced FHB. The purpose of this paper is to analyze risk and determine risk premiums necessary to induce growers to adopt risk reducing technologies in the case of wheat and barley grown in the United States. [ABSTRACT FROM AUTHOR]

In this paper, we evaluated the performance of the model AquaCrop for crop yield forecasting in the Brazilian semiarid (BSA) using meteorological observation and Eta model seasonal climate forecasts as input data. The study area is characterized by low rainfall that is poorly distributed throughout the rainy season; thus, the region's agricultural productivity is vulnerable to climate conditions. AquaCrop was first calibrated using field experiments and subsequently applied to simulate an operational crop yield forecast system for maize under rainfed conditions. Simulations were performed with daily data for 37 growing seasons for the period 2001–2010. The seasonal climate forecast was used in combination with observed meteorological data to anticipate the crop forecast. Soil characteristics were derived from pedotransfer functions (PTFs). We were able to demonstrate the ability of the seasonal crop yield forecast system to provide timely and accurate information about maize yield at least 30 days in advance of the harvest. The development of improved crop yield forecasting system is crucial for implementing drought-preparedness measures in the BSA region. [ABSTRACT FROM AUTHOR]

The agricultural sector in Lao PDR is forecast to move from subsistence rice production to a more modernized and market-oriented sector with greater focus on commercialization of agricultural production. Intensification of agricultural production in the southern and central rice growing regions of Lao PDR is problematic as dryland farmers rely on rainfall and soils are poor, yet rural households have been experiencing rapid change in their farming and livelihood systems. This paper employs Q methodology techniques to explore 35 farmers' viewpoints when contemplating their production goals and potential to adopt technologies to improve productivity. Findings describe the two emerging viewpoints among farmers as ‘labour saving productivity maximization’ and ‘traditional labour productivity using improved techniques’. The two viewpoints describe the different issues currently guiding production decisions. While the Lao Government forecasts substantial increases in rice production in the southern plains, farmers will require specialized and tailored support, accounting for their envisaged livelihood and production goals, to allow the sector transformation that many stakeholders currently envisage. [ABSTRACT FROM AUTHOR]

Maintaining cereal production in the Bekaa valley in Lebanon presents a serious challenge. Lack of water is the driving force of agricultural research which is mainly focused on introduction of drought resistant cultivars, application of conservation tillage and supplemental irrigation. In this context forty-eight experimental plots were laid out for three years in a statistical split plot design. The statistical analyses showed that aboveground biomass and yield were significantly affected by irrigation for barley but not for the yield of durum wheat. Effects of soil tillage practices and introduction of new cultivares were not significant. A soil survey indicated that the implicit assumption of soil homogeneity of the agronomic design was correct for surface soil but that two different soil types (Cambisols and Fluvisols) had to be distinguished considering subsoil conditions and corresponding rooting patterns. Therefore, the main objective of this paper was to determine the effects of different soil types on crop response and, in addition, to assess how physically-based modeling can predict future effects of climate change on crops and soils. Simulation model SWAP was validated for local conditions using measurements of soil water contents, aboveground biomass and yield of wheat. Considering two rather than one soil type for the experimental area resulted in different conclusions for both crops as to the effectivity of both conservation tillage and irrigation, demonstrating that a distinction of only one soil type results in misleading results. The validated model was applied to estimate yields considering climate change, focusing on the application of supplemental irrigation. Yields for “Mikii3” a durum wheat cultivar are expected to increase by appr. 14% in both soils due to climate change. More importantly, only 3 supplemental irrigations would be needed for the deep soil requiring 5% more water as compared with current climate trend, while the shallow soil needs 13 irrigations, corresponding with a need for 35% more water. This is highly significant from an economic point of view and supports the relevance of distinguishing two soil types. It was demonstrated the synergy of joint research by the agronomic and soil science community and the need for executing a soil survey in future when planning agronomic experiments, including a hydrological soil characterisation. [ABSTRACT FROM AUTHOR]

Frequently acknowledged as coming forward to environmental issues by reducing external input use, low input (LI) dairy farming is gaining attention. The absence of a clearly delineated description of LI dairying, however, hampers identification and analysis of these farming systems. This paper aims at empirically examining, EU wide, the farm structure, production intensity and productivity of LI with respect to their high input (HI) conventional counterpart and to organic dairying (ORG). A pragmatic quartiles-based categorization of farms from the Farm Accountancy Data Network of 20 important EU dairy countries, with the value of external input costs per grazing livestock unit (GLU) is used as prior discriminating indicator between LI and HI. LI dairy farms are smaller than HI dairy farms, in particular when farm size is expressed as total farm capital. Other variables that differentiate between LI and HI in most countries are number of dairy cows per GLU and area of forage and grassland on total agricultural area. Partial productivities in HI farms exceed those in LI farms, most apparent is milk production per cow. Differentiation of forage production between LI and HI is less uniform throughout Europe. A pairwise matching of differentiation profiles between countries indicates that differentiation between LI and HI is country specific. A similar diversity in country-specific differentiation between ORG and LI farming is found. [ABSTRACT FROM AUTHOR]

Livestock provides an important source of income and nourishment for around one billion rural households worldwide. Demand for livestock food products is increasing, especially in developing countries, and there are opportunities to increase production to meet local demand and increase farm incomes. Estimating the scale of livestock yield gaps and better understanding factors limiting current production will help to define the technological and investment needs in each livestock sector. The aim of this paper is to quantify livestock yield gaps and evaluate opportunities to increase dairy production in Sub-Saharan Africa and South Asia, using case studies from Ethiopia and India. We combined three different methods in our approach. Benchmarking and a frontier analysis were used to estimate attainable milk yields based on survey data. Household modelling was then used to simulate the effects of various interventions on dairy production and income. We tested interventions based on improved livestock nutrition and genetics in the extensive lowland grazing zone and highland mixed crop-livestock zones of Ethiopia, and the intensive irrigated and rainfed zones of India. Our analyses indicate that there are considerable yield gaps for dairy production in both countries, and opportunities to increase production using the interventions tested. In some cases, combined interventions could increase production past currently attainable livestock yields. [ABSTRACT FROM AUTHOR]

This paper describes an investigation of various adaptations of rice based cropping systems to climate variability in India's Telangana State. All adaptations were generated through participatory engagement and were field-tested with local smallholder households before being evaluated through cropping system simulation analysis. This approach contrasts with most research about adaptation of cropping systems to climate variability and climate change that is mostly based on simplifying assumptions about current farmer management practices and where the feasibility of implementing proposed adaptations is rarely tested. In this study, the investigation commenced with discussions about climate related issues in rice based farming systems between researchers, farmers and NGOs in three villages in three Mandals of the state of Telangana. Participatory intervention was used to identify new practices that could provide more adaptive and robust responses to climate variability. Suggested adaptations were implemented in on-farm experimentation. Fields demonstrating these adaptations were monitored and results were discussed with participating farmers at regular ‘Climate Club’ village meetings. Crop and soil data from these fields were used to locally parameterise the cropping systems simulator APSIM. Local adaptations that were trialled in the villages were simulated using local soil and long term historical weather data. In each of the case studies, a number of adaptations that were developed and implemented in the villages were shown through simulation to be successful in terms of agricultural production, stability of yields and resource use efficiency. Of the adaptations investigated, sowing rules to reduce the chance of crop failure due to early dry spells were most readily adopted and are also relatively easy to extend to other villages. Strategic irrigation of rainfed crops such as maize and cotton resulted in significant gains to profitability and stability of these crops but cannot be considered in isolation where access to water is limited. Reduced irrigation of rice resulted in over 60 mm/ha/yr. savings in water and some improvements in gross margins but this adaptation was not popular with farmers due to its burden on labour and added risks associated with unreliable supply of electricity for pumping at critical times. The reduced rice area for strategic irrigation of rainfed crops adaptation resulted in improved gross margins per hectare per year and higher net water productivity. This adaptation is most promising but will require institutional change around water use policy and more equitable allocation of limited water resources within villages. These results led us to the proposition that participatory action research with smallholder farmers, coupled with field-testing and simulation analysis can produce practical and productive adaptations to climate variability. [ABSTRACT FROM AUTHOR]

Climate change is expected to adversely affect agricultural production, particularly in Iran where the agricultural sector forms the backbone of the economy. This is true for the agriculture sector of the southwest region of Iran which is largely rain-fed and dominated by smallholder farmers with minimal livelihood alternatives. Although several studies have been conducted on climate change, little has been done on the adaptive capacity of farmers within farming systems, especially in Iran. The main objective of this study was to evaluate the adaptive capacity of four common farming systems (i.e., family farming system, cooperative farming system, commercialized farming system, and agro-enterprise farming system) in Fars province, southwestern Iran. A seven-step approach was used to normalize, weigh, and aggregate 102 individual indicators within a composite adaptive capacity index that is dimensionless, ranging from 0 to 1, and 1 is the optimal value. The required data were collected using a cross-sectional survey from 1472 farmers within the four mentioned farming systems. The results showed that the highest value was recorded by agro-enterprise for knowledge, perception and awareness, transport and physical accessibility, and institution and economic resources. Agro-enterprise scored higher in terms of awareness and action, which shows that these farming systems have a higher capacity for adaptation to the threat posed by climate diversity. Farmers in this farming system benefit from high formal education, high perceptions about the threat and risk of climate change, the occurrence of climatic events, causes for the change in climatic parameters, and awareness of climate change impacts. This paper recommends resilience-building interventions in the study area, which target individuals with low adaptive capacities, especially farmers. The results can also assist agricultural policymakers to recognize which components and determinants of adaptive capacity should be prioritized to mitigate the threat of climate change to farming systems. [Display omitted] • This study aimed to investigate the adaptive capacity of common agricultural systems • Climate change has a negative impact on Iran's agricultural production systems • The results showed that farmers at agro-enterprises have a well awareness of climate change adaptation • It is essential to obtain information about potential, effective, and cost-effective adaptation options [ABSTRACT FROM AUTHOR]

This paper explores agriculture and nutrition linkages in Bangladesh, a country that achieved rapid growth in rice productivity at a relatively late stage in Asia's Green Revolution, as well as unheralded progress against undernutrition. To do so, we first outline a simple conceptual model to identify the different impacts that productivity growth in a food staple(s) might have on child nutrition outcomes, with a particular focus on changes in diets at the household and child level. We then apply this framework to a descriptive overview of the evolution of Bangladesh's food system in recent decades. We show that this evolution is characterized rapid growth in yields and calorie availability, but relatively sluggish diversification in both food production and consumption, despite increasing reliance on imports for dietary diversification. Next, we create a multi-round district level panel that links changes in nutrition survey data with agricultural sample survey data over 1996–2011, a period in which rice yields rose by more than 70%. We then use this panel to more rigorously test for associations between yield growth and various anthropometric and child feeding indicators. Consistent with our descriptive evidence on dietary changes, we find that rice yields predict the earlier introduction of complementary foods to young children (most frequently rice) as well as increases in their weight-for-height, but no improvements in their dietary diversity or height-for-age. Since Bangladesh has one of the highest rates of child wasting in the world, these significant associations between yields and child weight gain are encouraging, but the lack of discernible effects on children's dietary diversity or linear growth is cause for concern. Indeed, it suggests that further nutritional impacts will require diversifying the Bangladeshi food basket through both supply and demand-side interventions. [ABSTRACT FROM AUTHOR]

High-input agriculture in China has successfully increased crop productivity in the past decades, but at a significant environmental cost. It is essential to improve management strategies to mitigate greenhouse gas (GHG) emissions and other environmental costs, while maintaining grain yields. However, there is a lack of studies to evaluate mitigation strategies under long-term climate variability. This paper combines field experimental data and soil–plant systems modeling to investigate the potential for improving water and nitrogen management of a wheat–maize double cropping system in North China Plain. The APSIM model was calibrated against the data and then applied to simulate crop yield and N 2 O emissions from soil in response to irrigation and nitrogen inputs. Our results show that the N fertilizer rate and irrigation amount under the local farmer practice could be reduced by 28% and 14% without sacrificing crop yield. This in turn led to a reduction in GHG emissions by 31%, mainly attributed to the decrease in emissions from the production and transportation of N fertilizer and direct N 2 O emissions from soil. Additionally, the results indicate that the direct N 2 O emissions from soil was positively correlated with N inputs, implying an increasing emission factor (N 2 O produced per unit of N input) with N application rates. It is concluded that potential exists to optimize N fertilizer rate and irrigation amount to reduce GHG emissions while still maintaining crop yield in the agro-ecosystems in North China Plain. [ABSTRACT FROM AUTHOR]

Direct seeding mulch-based cropping (DMC) systems are often considered as an efficient way of combining ecological sustainability and economic viability while maintaining or increasing agricultural productivity. This paper describes a modelling analysis of the functioning of family farms in rural settlements of the agrarian reform in the Cerrados of Brazil. The aim was to assess the impact of the introduction of DMC systems with and without cover crops on crop-livestock management and net household income. A bio-economic farm model based on optimization of a utility function under multiple constraints was developed, capturing the interactions between livestock activities and the introduction of DMC systems. The model was run for six farms representing three farm types in the study area: 1) subsistence-oriented mixed crop-livestock farms; 2) market-oriented dairy farms; and 3) mixed crop-livestock farms, oriented to meat marketing. The following maize-based DMC systems were evaluated: a DMC system with mulch from residues of the previous maize crop and no cover crop, and two DMC systems with a fodder species, Brachiaria brizanta or Cajanus cajan , as cover crop. The simulated adoptability of DMC systems by farmers of the assentamentos depended to a large extent on the yields and feed quality of the fodder species. The modelling results suggested that DMC with C. cajan as cover crop was the best suitable option for all simulated farm types. This was mainly explained by its high feed value (expressed in crude protein content). Furthermore, in the process of intensification and specialization in dairy production, farmers were likely to shift from using maize for pig husbandry to feeding it to the dairy cows. The introduction of cover crops in the farm systems was a source of additional animal feed during the dry season that was cheaper than the purchase of feed concentrates, when the size of the dairy herd did not exceed a certain threshold. [ABSTRACT FROM AUTHOR]

In the Midwestern United States, where a third of the world's maize crop is grown, there are few decision support tools available to help farmers and their advisors plan for an uncertain climatic future. Developing tools that are actually useful and usable to agricultural decision makers necessitates an interdisciplinary team of climate scientists, agronomists, computer scientists, and social scientists. With such diversity come varying levels of engagement (e.g. co-project director, student, technician, etc.) and experience working with farmers and/or serving in an official Extension capacity. Therefore working together to address this challenging issue is not straight-forward. This paper reviews how a survey of a large interdisciplinary team working on developing decision support tools to ensure resilient maize production in this region identified differences between team members and helped improve team functioning and communication. Specifically the team survey revealed some important differences in how team members perceive farmers' use of climate information, the types of decisions that should be addressed with a tool, and how such tools should function. These differences can be primarily explained by disciplinary background and project role and have provided valuable opportunities to learn from each other and build consensus on decision support tools developed. The survey as a feed-back tool complements other team communication approaches and reminds the team of the need for continuous communication and frequent discussion of assumptions. [ABSTRACT FROM AUTHOR]

Combining remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data with Bangladesh Household Income and Expenditure Survey (HIES) data, this study estimates losses in rainfed rice production at the household level. In particular, we estimated the rice areas affected by drought and submergence from remotely sensed MODIS data and rice production from Household Income and Expenditure Survey (HIES) data for 2000, 2005 and 2010. Applying two limit Tobit estimation method, this study demonstrated that both drought and submergence significantly affected rice production. Findings reveal that on average, a one percent increase in drought affected area at district level reduces Aman season rice production by approximately 1382 kilograms per household on average, annually. Similarly, a one percent increase in drought area reduces rainfed Aus season rice production by approximately 693 kilograms per household, on average, annually. Based on the findings the paper suggests disseminating and developing drought and submergence tolerant rice and also short duration rice varieties to minimize loss caused by drought and submergence in Aus and Aman rice seasons. [ABSTRACT FROM AUTHOR]

Most definitions of organic farming emphasise a holistic approach that combines quality production with sustainable practices and positive impact on resource conservation, biodiversity and animal welfare. Its founding values were also connected to small-scale production, minimisation of external inputs use, diversification and short market circuits. In the last two decades, organic farming has grown very rapidly, resulting in the subordination of its values to market forces. There has been a greater specialisation, an increase of scale, the involvement of large multinational corporations and the inclusion in global trade. This conventionalisation process and the connected certification standards, primarily focussed on banning the use of pesticides and chemical fertilisers, may weaken the vision of organic farming as a more sustainable alternative to conventional farming. This paper aims to identify the factors that influence the choice of organic farmers for more sustainable practices that go beyond the strict limits imposed by certification. A probabilistic model was estimated using survey data collected from 352 Italian and Portuguese certified organic farmers. The results show that women and farmers longest engaged in organic farming are more likely to adopt sustainable practices. They also indicate that farm size, land ownership, the existence of some types of complementary activities and the sources of information used by farmers affect the adoption of such practices. [ABSTRACT FROM AUTHOR]

Feeding forages produced by early and frequent harvests may improve animal performance. This paper evaluates how harvesting regimes (HRs) in grass silage production influence optimal use of inputs and profitability in two types of Norwegian dairy farming systems: a mountain grassland farm and a lowland mixed crop–livestock farm. A whole-farm linear programming model was developed to compare three HRs within each farm type. HR1 and HR2 were three-cut systems harvested at very early (HR1) or early (HR2) crop maturity stages producing highly digestible forages. HR3 was a two-cut system returning higher dry matter yields of medium digestibility. Input–output response relations incorporated into the model were derived from field trials (N-fertilisation × HR), conducted at two representative locations for the two farm types, and from dairy cow and finishing bull feeding experiments at various levels of concentrate feeding to supplement silage from each HR. The model maximised total gross margin of farms with 150,000 l milk quota, and housing capacity for 25 cows. Farmland availability varied from 10 to 30 ha with 20 ha as the basis. The results indicated that farmland availability profoundly influences the input intensity and the profitability of producing and feeding silages harvested at early maturity stages. At restricted land availabilities in the mountain, the three-cut silages were obtained at too high costs in terms of lower grass yield, increased harvesting costs, and costs of shorter ley life. Silage DM consumption per head also increased with increasing digestibility. Under HR1 and HR2 it was impossible to fully produce the quota with 20 ha farmland and overall mountain farm profitability was depressed. With more land available, sufficient quantities of three-cut silages were produced to take advantage of the enhanced animal performances. Within all HRs, grass yields were highest in the lowland. The profitability of HR3 in the lowland was limited to smaller land areas, and, generally, producing highly digestible silages of HR1 was more profitable than devoting more land to barley. For both farm types, inputs of fertilisers and concentrates declined as more land became available, but at lower land areas for HR3. Removal of the milk quota constraint resulted in higher milk yields per cow, and strengthened profitability of HR3 endured into larger farmland areas than with a quota. With abundant land available, however, the three-cut silages were relatively more profitable without rather than with a restricting quota, and HR1 outperformed HR2. [ABSTRACT FROM AUTHOR]

Despite the great pressure on global natural resources, few LCA studies focus on total resource consumption and the efficiency of the use of those resources. Moreover, a total resource use assessment for agricultural systems is highly relevant because many of these systems have become high input/high output systems in order to achieve higher productivity. In this study, we propose a framework to evaluate total resource consumption of agricultural systems at the process level using an Exergy Analysis (EA) and at the life cycle level using an Exergetic Life Cycle Assessment (ELCA). We evaluate the applicability and usefulness of this approach based on a case study of an intensive confinement-based dairy farm in the region of Flanders, Belgium. The EA showed that more than half of the resources consumed by the dairy farm's herd was irreversibly lost, as a consequence of the second law of thermodynamics. The remaining went for almost two-thirds to manure (54%) and methane emissions (9%), while only one-third flowed to end-products, i.e. milk (32%) and the animals awaiting slaughter (2%). The ELCA identified the feed supply as the most demanding part of the dairy production chain by far, representing 93% of the resource footprint. Overall, concentrates were on average 2.5 times more resource-intensive per kg dry matter than roughages, while wet by-products were 34% and 73% less resource-intensive than roughages and concentrates, respectively. Mainly land (77%) and fossil resources (17%) were required throughout the life cycle. About 36% of the occupied land (in terms of m 2 *year) was located off-farm. Slightly less than one-quarter of the fossil resources were used on-farm as fuel and electricity. The on-farm use of groundwater accounted for about half of the total blue water use across the life cycle. With this paper, we show the usefulness of the proposed framework to evaluate total resource consumption of dairy farms and to identify on-farm and off-farm improvement opportunities. This framework has potential to support research on whole-farm improvement strategies such as pasture-based systems and low-input farming, and to compare populations of contrasting milk production systems. [ABSTRACT FROM AUTHOR]

Crop residues (CR) have become a limited resource in mixed crop-livestock farms. As a result of the increasing demand and low availability of alternative resources, CR became an essential resource for household activities, especially for livestock keeping; a major livelihood element of smallholder farmers in the developing world. Farmers’ decisions on CR use are determined by farmers’ preferences, total crop production, availability of alternative resources and demand for CR. Interaction of these determinants can result in pressures and trade-offs of CR use. Determinants, pressures and trade-offs are shaped by the specific socio-economic and agro-ecological context of these mixed farms. The objective of this paper is to provide a comparative analysis of the determinants of CR use and to examine some options to cope with pressures and trade-offs in 12 study sites across Sub-Saharan Africa and South Asia. Drawing on socio-economic data at household and village level, we describe how cereal intensification and livestock feed demand influence use, pressures and trade-offs of CR use across study sites, specifically cereal residue. Our results show that in low cereal production and livestock feed demand sites, despite a low demand for CR and availability of alternative biomass, pressures and trade-offs of CR use are common particularly in the dry season. In sites with moderate cereal production, and low–moderate and moderate livestock feed demand, alternative biomass resources are scarce and most residues are fed to livestock or used to cover household needs. Subsequently, pressures and potential trade-offs are stronger. In sites with low cereal production and high livestock feed demand, pressures and trade-offs depend on the availability of better feed resources. Finally, sites with high cereal production and high livestock feed demand have been able to fulfil most of the demand for CR, limiting pressures and trade-offs. These patterns show that agricultural intensification, better management of communal resources and off-farm activities are plausible development pathways to overcome pressures and trade-offs of CR use. Although technologies can largely improve these trends, research and development should revisit past initiatives so as to develop innovative approaches to tackle the well-known problem of low agricultural production in many smallholder mixed systems, creating more sustainable futures. [ABSTRACT FROM AUTHOR]

Information on irrigation indicators is needed to improve irrigation management. The objective of this paper is to assess the irrigation performance of the Gezira Scheme, Sudan. Data on climatic elements, irrigation water supply and crop yield for the period 1961-2010 integrated with remote sensing data from 2000 to 2014 are used. Crop evapotranspirations (ETc) are determined by FAO 56 reference evapotranspiration (ETo) with derived crop coefficients (kc) and the actual evapotranspiration (ETa) is estimated using FAO 33. Irrigation efficiency indicators, Relative Irrigation Supply (RIS) and Relative Water Supply (RWS), are calculated. Potential rainwater supply to crop is tested based on Moisture Availability Index (MAI) and Ratio of Moisture Availability (RMA). Furthermore, Water Use Efficiency (WUE) is used to assess the water productivity. For the spatial drought assessment, a modified Vegetation Condition Index (MVCI) derived from remotely-sensed data is used. The results of RIS and RWS show respectively that the irrigation efficiency has decreased since 1993/1994 from 1.40 and 1.70 to 2.23 and 2.60, respectively. MAI for cotton and sorghum presents moderate deficit values (0.4 to 0.68) in July and August. RMA has an average value of 0.67, 0.49 and 0.46 in July for cotton, groundnut and sorghum, respectively, indicating a promising rainfall contribution to irrigation. The Gezira Scheme achieves low WUE in comparison to many irrigation schemes of the globe. Based on the MVCI analysis, the northern part of the scheme experiences characteristic drought during the summer crop season whereas irrigation water is abundantly available during the winter. In conclusion, the overall water availability is higher than the demand; most likely the rain is not considered in irrigation scheduling. Low productivity is mainly due to poor distribution and irrigation mismanagement. Rain and drained irrigation water harvesting could be addressed in the scheme for balancing the demand and supply. [ABSTRACT FROM AUTHOR]

The paper presents a model for integrated sustainability assessment at a farm scale. The SOSTARE model (analysis of farm technical efficiency and impacts on environmental and economic sustainability), in fact, aims at providing a diagnostic tool to farmers and advisory services to assess the general performance of the farm, explore in detail any perceived weaknesses in farm management and to investigate the impact of changes that might improve efficiency. The model is derived from a survey of 68 farms. The sampled farms have been selected in order to represent the panorama of farm typologies that are characteristic of the agriculture in one of the most intensively cultivated areas of Europe, the Po plain in Northern Italy, and include multi-functional and non-multi-functional farms, low-input and conventional management typologies, subdivided into production of rice, cereals, and livestock (meat and milk). The SOSTARE model is based on a set of indicators, which are aggregated in a stepwise fashion to provide the user with an immediate valuation of a farm's performance. The aggregation framework was structured on the following steps: statistical analysis of all the collected input data and indicators to identify relevant variables and reduce redundancy; identification of the range of variation of the selected basic indicators and normalisation; definition of weights through statistical analysis and expert judgement, aggregation. The result is a synthetic indicator for each of the three analysed aspects of sustainability (economic, agronomic and ecological), which are decomposed into a total of twelve sub-dimensions. These include: fertilisers, water and agrochemical management; cropping system; energy input; value of production; value added; farm household income; CAP independence; farm business diversification; natural value of the farm and functional landscape pattern. In practical terms the output of the model is provided to the user as original indicator values in the form of tables, bar diagrams corresponding to each aggregation step and a radar diagram representing the overall farm performance in the twelve sub-dimensions. Furthermore, results can be related either to the optimal performance or to a reference situation (i.e. the average of the farm typology, the average of all farms). Results show that the model is able to react to different types of farm management that were studied in the reference sample of farms and that weaknesses of farm performances can be clearly identified. Moreover, its application over time would provide information that could be used to assess and monitor policy performances at a regional level. [ABSTRACT FROM AUTHOR]

Parasitic weeds such as Striga spp and Rhamphicarpa fistulosa in smallholder rice production systems form an increasing problem for food and income security in sub-Saharan Africa. In this paper we implement the Rapid Appraisal of Agricultural Innovation Systems (RAAIS) as a diagnostic tool to identify specific and generic entry points for innovations to address parasitic weeds in rain-fed rice production in Tanzania. Data were gathered across three study sites in Tanzania where parasitic weeds are eminent (Kyela, Songea Rural and Morogoro Rural districts). The results demonstrate that in Tanzania, weeds in general and parasitic weeds in particular receive little attention in agricultural research, training and education curricula. Crop protection policies mainly focus on the control of (insect) pest and diseases and there is relatively little attention for weed prevention, which is essential for addressing parasitic weed problems effectively. Specific entry points for innovation include increasing awareness of parasitic weed problems among farmers, extension and crop protection officers and policymakers. In regions where awareness is relatively high, participatory research approaches can provide a basis for developing locally adapted parasitic weed management strategies. Generic entry points for innovation include enhanced collaboration and interaction between stakeholders across different levels, for example in multi-stakeholder platforms. This can provide the basis for developing and implementing coherent policy and development strategies to address structural constraints in the agricultural system, including the promotion of clean local seed systems, investments in physical and knowledge infrastructure development, adequate backstopping of agricultural extension officers, agribusiness training for farmers, quality control of agricultural inputs, timely access to agricultural inputs, and improved access to markets for farmers. Together the specific and generic entry points can strengthen the innovation capacity of Tanzania's agricultural system to address parasitic weed problems, as well as other complex agricultural problems. [ABSTRACT FROM AUTHOR]

Measuring farmland fragmentation and its interactions with anthropogenic activities can advance our understanding of complexity in agricultural systems. Majority of previous studies focused on farmland ownership fragmentation rather than physical landscape fragmentation. This paper characterized the farmland landscape fragmentation dynamics in Tiaoxi watershed (China) from 1985 to 2013 using a set of variables (area-weighted mean patch area, patch density, area-weighted mean shape index, mean Euclidean nearest neighbor distance, splitting index, and effective mesh size). Four categories of anthropogenic drivers (demographic, economic, social and cultural, and scientific and technological) and their relative importance were quantified by multiple regression and variance partitioning. Results showed a linear increasing trend of farmland fragmentation in Tiaoxi watershed during the study period. Drivers for farmland fragmentation differed with variables. In general, non-agricultural population and migration population were the key demographic drivers, while road mileage and investment in real estate were the principal social drivers. Two groups of economic drivers were identified: one group included fruit and seafood production, another included per capita income and proportion of tertiary industry. Besides, education expenses increases and technological improvement could significantly reduce farmland fragmentation. Considering the relative importance for different categories of drivers, economy was the most influential driver; its joint influences with social and cultural drivers and those with scientific and technological drivers were relatively stronger. Our study advanced the understanding of principle anthropogenic drivers influencing farmland fragmentation dynamics. [ABSTRACT FROM AUTHOR]

Agricultural production in “open-sky systems” such as extensive cattle ranching on natural grasslands is subject to inter-annual climatic variability and other market fluctuations. To tackle the dependency of livestock breeding on these factors, we conducted participatory modeling with cattle producers in Uruguay. The methodology consists of simulating possible scenarios to collectively evaluate the different herd management practice alternatives. In this paper, we present an Agent-Based Model built with stakeholders and designed to represent a breeding system on a typical extensive grazing area in the basaltic soils region (BR) of Uruguay. This model has three main modules: environment, biophysical and decisional sub-models. This modularity allows the conducting of virtual experiments to reveal how some herd management decisions (such as seasonal stocking rate adjustments) combined with a climatic series can result in resilience against drought periods and market movements. Long-term simulations were implemented to analyze the sensitivity of the model to key management parameters with varying climate conditions. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. Rigid strategies are bound to fail and cause systems to break. Adaptive management emerged as a critical option for the sustainability of livestock breeding. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. This result highlights the importance of adaptive management, one that can react to a changing environment, for the sustainability of livestock breeding. [ABSTRACT FROM AUTHOR]

Eco-efficiency offers a promising approach for the sustainable intensification of production systems in Sub-Saharan Africa. Data Envelopment Analysis (DEA), which is widely used for eco-efficiency analyses, is however sensitive to outliers and the analysis of the influence of external factors in the second stage requires the separability assumption to hold. Order- m estimators are proposed to overcome those disadvantages, but have been rarely applied in eco-efficiency analysis. This paper assesses the eco-efficiency of smallholder perennial cash crop production in Ghana and Kenya. It examines factors influencing eco-efficiency scores and in doing so, tests the application of order- m frontiers as a promising method for eco-efficiency analysis in the agricultural context. The analysis is performed for four selected perennial crop cases, namely cocoa, coffee, macadamia, and mango, applying DEA as well as the order- m approach to a comprehensive empirical dataset. Seven relevant environmental pressures as well as determining factors around capacity development, farm and farmer features, and crop production environment are considered. The distribution of eco-efficiency estimates among coffee farms showed the widest spread, which indicates the greatest potential to increase eco-efficiency. However, also the dispersion of scores within the other crop cases suggests room for improvements of eco-efficiency within the current production context. The subsequent analysis of determinants based on the order- m scores revealed that eco-efficiency scores were strongly influenced by variables, which measure capacity development, and resource endowments, such as labor and land, whereas the crop production environment had some influence, but results were unspecific. Generally, a positive effect is highly context-specific. The results underline the importance of designing effective training modalities and policies that allow knowledge to be put into practice, which involves the creation of marketing opportunities, the provision of targeted and regular advisory services, as well as region-wide measures to build and maintain soil fertility in a sustainable manner. To our knowledge, this study presents the first attempt to apply inputoriented order- m frontiers to assess eco-efficiency in the agricultural context, comparing its eco-efficiency rankings to those estimated with the widely applied DEA approach. This can inform the discussion on robust eco-efficiency assessments. [Display omitted] • While increased eco-efficiency matters for sustainable intensification, eco-efficiency studies of agriculture in Sub-Saharan Africa are lacking. • We estimated eco-efficiency scores in smallholder crop production, testing the use of order- m frontiers and comparing it to the widely applied DEA. • Correlations between eco-efficiency rankings of the DEA and the order- m approach were positive and significant, albeit not strong. • Order- m efficiency scores are influenced by resource endowments, capacity development and production environment but effects are context-specific. • Study represents the first application of input-oriented order- m frontiers to assess and explain eco-efficiency in the agricultural context. [ABSTRACT FROM AUTHOR]

Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops. [Display omitted] • Crop model is a tool to simulate crop growth, seek solutions for mitigating negative environmental impacts on production. • APSIM Classic has been used as an example to explore wheat yield prediction performance and the influential factors. • Reviewed and established a meta-database from 76 published studies. • Overall: the model predicts wheat yield with RMSE=1 t/ha. Fully calibrated and under stress-free condition: RMSE=0.64 t/ha. • Calibration method, heat, frost, water, and nitrogen stresses were identified as factors causing the model to mis-simulate. [ABSTRACT FROM AUTHOR]