Search

In Europe, increasing the area of legume crops has been identified as a key measure to achieve the objectives set by the European Green Deal and transition toward more sustainable food systems. Although the role of grain legumes in climate change mitigation has been closely examined, little research has focused on how climate change will challenge the development of these crops. This article systematically reviews recent simulation studies to assess the impact of climate change on grain legume performances in Europe and the effect of adaptation strategies. Forty papers using process-based, ecological niche, or statistical models were selected to simulate the response of eight grain legume species to future climate (2020–2100) in Europe. The lack of data on adaptation strategies in Europe was compensated for by enlarging the study area to climatically similar regions. The review highlights a notable imbalance between research about soybean versus other grain legumes, with soybean representing approximately 80% of selected studies. Studies focused on soybean found good agreement, with yield or suitability gains found in northern Europe and a higher probability of yield losses in southern and south-eastern Europe. While a similar spatial pattern may be expected for other grain legumes, the scarcity of data makes this result more uncertain. The review also shows that several adaptation strategies have the potential to mitigate the negative impact of climate change on grain legume performances or enhance its positive impact. The most promising strategies tested include irrigation, change in sowing date, and cultivar choice. In addition, we identify several knowledge gaps that, if addressed, would support legume development in Europe. In particular, key species such as field pea, faba bean, lentil, and chickpea remain blind spots, despite their prominent role in European environmental, agricultural, and nutritional policies. Other knowledge gaps include a lack of accounting for crop response to elevated CO 2 , ozone, and future biotic pressure, and a limited range of adaptation strategies tested and indicators assessed. Implementing multi-criteria analyses that involve stakeholders would help identify relevant inputs and outputs for future simulations. • We review 40 papers assessing grain legume response to climate change in Europe. • Grain legumes other than soybean are blind spots despite their role in policies. • For soybean, yield gains are expected in the north and yield losses in the south. • Adaptation strategies can mitigate the impact of climate change on grain legumes. • We identify several knowledge gaps, including a lack of data on key legume species. [ABSTRACT FROM AUTHOR]

Agriculture has both witnessed and caused climate change. Therefore, building climate resilience in the food production system is mandatory. Currently in Sri Lanka, recommended climate-smart agricultural practices recommend a common package to the entire country. However, considering the diversity that occurs across the country in every social, and ecological aspect it is essential to understand the cultural, ecological, and socio-economic conditions of the country to determine more efficient agricultural practices to improve the resilience of the agricultural systems in a particular region. To achieve this objective, numerous traditional methods can be employed in modern farming strategies in order to make it more nature-friendly. Therefore, this literature review was developed to provide insights for the development of an evidence-based framework to enhance the climate resilience of Sri Lanka's agricultural systems. In that aspect, 36 scientific communications were considered over 13 years, using articles from the Google Scholar, ScienceDirect, and Web of Science databases (2010–2023). In addition, 4 country papers, and publications from the UN, ADB, FAO, and IPCC were cited. In this literature review article, the relationship between agriculture and climate change was identified. Then, the climate-resilient ability of traditional agriculture and its location specificity were reviewed. The status of climate resilience in the existing food system was analyzed considering the Batticaloa farming system as a case study. Finally, an appropriate location-specific framework was suggested to build climate resilience by integrating traditional farming techniques into modern agriculture. As per the results, rainwater harvesting strategies as cascade village tank system, management of limited water through practices as shared cultivation (Bethma govithena), shared responsibility (Pangukariya) for the maintenance of village tanks, dry seed paddy farming (Kekulam govithena), maintaining agro-biodiversity via, mixed cropping, agro-forestry, use of resistant crop varieties are the key Sri Lankan traditional agricultural practices which can be adapted to build the system resilience. These practices are developed based on the specific cultural, ecological, and socio-economic conditions of each region of the country. However, the establishment of micro-irrigation systems, utilizing early warning systems, development of high-yielding resistant crop varieties, crop-livestock integration, and cultivation of location-specific crop varieties are the main modern agricultural practices recommended to build up the climate resilience of the food systems. However, many of these recommended practices were exercised during the traditional agricultural systems as well. There are many other specific practices available that can be applied with modification even for present-day agriculture to enhance its climate resilience. Integrating the knowledge from traditional agriculture into modern agricultural practices will directly facilitate the achievement of three sustainable development goals of climate action, zero hunger, and no poverty through the development of regional-specific sustainable agricultural practices that restore the resilience of agriculture. Therefore, this literature review article proposes a framework for managing location-specific climate-resilient food systems on par with the regional agroecological and socioeconomic conditions of Sri Lanka. • Climate change adaptation and mitigation strategies were introduced in Sri Lanka. • Adaptation and mitigation strategies are site-specific. • Many traditional agricultural practices maintain equilibrium between nature and farming. • Wisdom of traditional agriculture and modern agricultural practices should be integrated. [ABSTRACT FROM AUTHOR]

The main objective of the CHIP project was to perform ‘standardised’ investigations of potato (Solanum tuberosum L. cv Bintje) responses to increased O3 and CO2 concentrations by means of open-top chambers (OTC) and free air carbon dioxide enrichment (FACE) systems. The experimental sites are located across Europe representing a broad range of different climatic conditions. In 1998 and 1999 a total number of 12 OTC experiments and four FACE experiments were conducted. According to the specific needs for subsequent modelling purposes, environmental data were collected during experiments, i.e. air temperature, global radiation, air humidity (vapour pressure deficit (VPD)), soil moisture and trace gas concentrations. In the present paper, the results of these measurements are summarised. It was shown that the experiments covered a considerable range of growing season mean air temperatures (13.8–19.9 °C) and global irradiances (12.0–21.3 MJ m−2 per day), the most important driving variables for crop growth simulation models. Analysis of the soils used during the experiments demonstrated that in most cases sufficient nutrient elements were available to guarantee an undisturbed growth. Mean concentrations of CO2 and O3 in ambient air and in different treatments illustrate the observed variability of trace gas exposures between different sites and experiments. However, the effects of these parameters on growth and yield are subject of separate papers. The general climatic conditions across Europe are also causing important growth and yield effects. Comparison of marketable tuber yields revealed an increase at higher latitudes. This result was associated with lower temperatures and VPD and longer day lengths at the higher latitudes, which in turn were associated with longer growing seasons. [Copyright &y& Elsevier]

Highlights • We identify four generations of model development from the 1950′s to 90′s. • Model application then expanded rapidly, especially for climate change in the 2010′s. • Innovation in model development slowed during this time, with application the focus. • Evidence of practice or policy impact of modelling is scarce, though an important goal. • Big data will cause future blending of mechanistic and empirical statistical modelling. Abstract Crops and cropping systems are characterised by complexity and variability. Complexity arises from inherently complex plant and soil processes combined in an almost infinite set of permutations and combinations associated with biotic and abiotic drivers that are inherently variable in both space and time. Modelling has evolved over the last 100 years as a means of describing and interpreting complex and variable performance and increasingly as a means of predicting likely performance in prescribed circumstances for better decision making. In this paper we reflect on the evolution of quantitative approaches to describing and predicting crop growth and cropping system performance. We begin with early mathematical descriptions of plant and crop growth and soil processes dating from the 1920′s to 1950′s. We explore the early crop models of the 60′s and 70′s and the more comprehensive crop-soil models of the 1970′s and 80′s. Cropping systems models with comprehensive systems management capabilities underpinned by a modular design began to gain currency in the 1990s. Over this long period, the ambitions held by model-makers' for model applications grew. We analysed the publication records of major cropping systems models to summarise the broad trends in model applications that emerged in the early 21st Century, based on the 60 years of development in the 20th Century. There was an "explosion" in publications on cropping systems models, with an eight fold increase from 2000 to 2015. In parallel, the application of models greatly expanded from approximately four areas in 2000 (agronomy, model development, climate change and methods) to more than 20 in 2015. However, despite this explosion and expansion there is little evidence in the literature that modelling was having an impact on farmers and policy makers. We conclude with an examination of the forces shaping cropping systems model development and application. Developments in data acquisition and model-data fusion may open the way for cropping systems models to have greater impact in real-world policy or practice settings, making a meaningful contribution to future agricultural productivity and sustainability. [ABSTRACT FROM AUTHOR]

Ridge-furrow mulching can reduce soil evaporation and conserve rainfall, thereby increasing crop yield and water use efficiency (WUE) in dryland cropping systems. In this study, we collected 837 observations from 50 published papers and used meta-analysis to investigate whether ridge-furrow and mulching practices are equally effective on maize yield, WUE and evapotranspiration (ET) across a range of precipitation and temperature gradients and soil types in China. Five practices were included: (i) straw mulch on flat plots (SMF), (ii) straw mulch on ridge-furrow plots (SMR), (iii) plastic mulch on flat plots (PMF), (iv) plastic mulch on ridge-furrow plots (PMR), and (v) flat plots without mulch, which was used as a control (CK). The meta-analysis showed that both straw mulch and plastic mulch significantly increased maize yields and WUE (except for SMR), and that plastic mulch was more effective than straw mulch in increasing yields, particularly in cold and dry environments. PMR has the highest yields and is more effective in clay loam than in silt loam soils. Straw mulch, but particularly plastic mulch, increased the soil moisture compared to the CK, while plastic mulch increased soil temperature, mainly in spring. However, the positive effect of plastic mulch on maize yield diminished with increasing mean growing-season temperature and precipitation, reaching zero (similar to the CK of no mulch and flat plots) when the growing-season precipitation was greater than 770 mm and the mean growing-season air temperature exceeded 24 °C. The small benefit of straw mulch (on average about 12%) was similar across the precipitation and temperature gradients, including when the benefits of plastic mulch reached zero. While our analysis has shown the benefits of ridge-furrow plastic mulch on yield and water-use efficiency, it has also highlighted the limitations of the benefits. The results provide a guide to the regions where plastic-film mulch and ridge-furrow planting are likely to improve maize yields and regions where the benefits are likely to be limited. [ABSTRACT FROM AUTHOR]

Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input variables. Several statistical regression methods were fitted to a dataset including 689 observations of winter oilseed rape yield from replicated field experiments conducted in 239 sites in Denmark, covering nearly all regions of the country from 1992 to 2013. Regression methods were compared by cross-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C temperature increase during flowering was estimated to range between 0 and +6 %, depending on choice of regression method. Precipitation was found to have an adverse effect on yield during autumn and winter. It was estimated that an increase in precipitation of +1 mm/day would result in a relative yield change ranging from 0 to −4 %. Soil type was also important for crop yields with lower yields on sandy soils compared to loamy soils. Later sowing was found to result in increased crop yield. The estimated effect of climate on yield was highly sensitive to the chosen regression method. Regression models showing similar performance led in some cases to different conclusions with respect to effect of temperature and precipitation. Hence, it is recommended to apply an ensemble of regression models, in order to account for the sensitivity of the data driven models for projecting crop yield under climate change. [ABSTRACT FROM AUTHOR]

Crop simulation models play an increasingly important role in agricultural management. Therefore, improving crop models' accuracy and reliability has become a key issue. Previous studies have shown that multi-model ensembles (MMEs) perform better than individual models and are capable of assessing model uncertainty. Here, we study the use of the Bayesian model averaging (BMA) method to improve the simulation performance of rice phenology models. The main objective of this paper is to apply BMA to five individual rice phenology models (CERES-Rice, the beta model, ORYZA2000, the rice clock model, and the simulation model for the rice-weather relationship) and to compare its simulation performance with an MME based on the mean value (Mean-MME). Furthermore, we quantify and evaluate the uncertainty of the individual and ensemble models. Finally, we analyze the prediction results of two ensemble models and five individual models under future climate scenarios. The result indicates that, compared with the accuracies of individual models, the accuracies of BMA and Mean-MME are 12.75% and 11.48% higher, respectively. BMA performs the best when the individual models had the best parameters, effectively optimizing the phenology simulation results of the individual models. For individual and ensemble models, the squared bias accounts for 48–88%, which is the largest contributor to overall prediction uncertainty. Under the future climate scenarios, the five models predict a range of 0.40–9.78 days in the phenological period, whereas the two ensemble models predict a smaller range of 0.54–1.63 days, which shows that the MMEs reduce the uncertainty of the individual models. We conclude that BMA is suitable for improving the accuracy and reliability of crop modeling. • Five widely used rice phenology models are calibrated and validated. • Two ensemble models are investigated to improve phenological simulation performance. • The prediction results of individual and ensemble models under future climate scenarios are analyzed. • We find that Bayesian model averaging effectively optimizes the phenological simulation results. • Uncertainties of individual models and ensembles are quantified and assessed. [ABSTRACT FROM AUTHOR]

Regionalized model calculations showed increased rainfall and temperatures in winter and less precipitation and higher temperatures in summer due to climate change effects in the future for numerous countries in the northern hemisphere. Furthermore, model simulations predicted enhanced weather variability with an increased risk of yield losses and reduced yield stability. Recently, double cropping systems (DCS) were suggested as an environmental friendly and productive adaptation strategy with increased yield stability. This paper reviews the potential benefit of four DCS (rye ( Secale cereale L.) as first crop and maize ( Zea mays L.), sunflower ( Helianthus annuus L.), sorghum ( Sorghum sudanense L. × Sorghum bicolor L.) and sudan grass ( S. sudanense L.) as second crops) in comparison with four conventional sole cropping systems (SCS) (maize, sunflower, sorghum and sudan grass) with regard to dry matter (DM) yield and soil water under conditions of climate change. We used the agro-ecosystem model HERMES for simulating these variables until the year 2100. The investigated crops sunflower, sorghum and sudan grass were parameterised first for HERMES achieving a satisfying performance. Results showed always higher DM yields per year of DCS compared with SCS. This was mainly caused by yield increases of the first crop winter rye harvested at the stage of milk ripeness. As a winter hardy crop, rye will benefit from increased precipitation and higher temperatures during winter months as well as from extended growth periods with an earlier onset in spring and an increase of growing days. Furthermore, rye is able to use the increased winter humidity for its spring growth in an efficient way. By contrast, model simulations showed that summer crops will be affected by reduced precipitation and higher temperatures during summer month for periods from 2050 onwards with the consequence of reduced yields. This yield reduction was found for all summer crops both in conventional sole crop and in DCS. Preponed harvesting of first crop winter rye as a consequence of earlier onset of growth period in spring under prospective climatic conditions lead to yield decrease, which could not be equalised by preponed sowing of second crops and extension of their growth period. Hence, total annual yield of both crops together decreased. The modification of sowing and harvesting dates as an adaptation strategy requires further research with the use of more holistic simulation models. To summarize, DCS may provide a promising adaptation strategy to effects of climate change with a substantial stabilisation of crop yields. [ABSTRACT FROM AUTHOR]

The yield gap (YG) between the potential yields (Yp) and the average on-farm yields (Ya) is an indicator of the potential improvement for crop production. Understanding how large the current gap is and how this gap has changed over the past few decades is essential for increasing wheat production to meet increased food demand in China. This paper describes a study conducted using an APSIM-Wheat model and farm-level crop yield to analyze the spatio-temporal distribution of the yield gap of winter wheat from 1981 to 2010 in the North China Plain. Nine varieties were calibrated and evaluated based on the data from 16 agro-meteorological experimental sites and then potential yields were estimated considering cultivar replacement. In addition, a trend pattern analysis of on-farm yields for the period 1981-2010 was conducted. Results revealed an estimated yield gap across the entire North China Plain region of 1140-6810 kg ha-1, with a weight average of 3630 kg ha-1 in 1981-2010. Expressed as a relative yield (yield gap % of potential yields), the range was 15-80%, and the weight average was 45%. Despite the negative effects of increasing temperature and decreasing radiation, the potential yields significantly increased by 45 kg ha-1 per year due to cultivar improvement. On-farm yields increased even more notably because of new cultivar selection, increased fertilizer application and other management improvements, but were stagnating in 32.3% of wheat areas, located mainly in Hebei province, Shandong province, Beijing and Tianjin. The improvement of on-farm yields have substantially contributed to yield gap spatio-temporal variation. As a result, the yield gap decreased from 4200 kg ha-1 (56%) in 1981-1990 to 3000 kg ha-1 (35%) in 2001-2010 at a rate of -69 kg ha-1 per year. However, yields stagnation will expand to the northern Henan province without cultivar potential productivity improving, where yield gap was close to or less than 20% of the potential yields and proved difficult to reduce. To further improve the total production of winter wheat in the coming decades, efforts should be paid to break the potential ceiling and reduce the yield gap by breeding higher yield variety and introduction of new agricultural technology. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies have shown that temperature rise leads to an earlier onset of spring in wild plant species and that farmers are not keeping track of climate change. Crop growth models and experiments show yield gains to be obtained from earlier sowing. Why do farmers not sow earlier? We propose simple models on the relation between several weather variables and farmers’ sowing dates and test these models with 392 observed weekly sugar beet sowing percentages in the Netherlands. Data showed no correlation between spring temperature and sowing dates. However when we corrected for number of frost days in spring, we clearly saw earlier sowing at higher temperatures. Frost causes crumbling of the soil resulting in a nice seedbed; farmers sow later when there have been less frost days. Temperature rise “makes” farmers sow earlier, but the number or frost days simultaneously decreases and this “makes” farmers sow later. The combined effect is that temperature rise leads to only a small net advance in sowing date, 2 days per 1°C according to our model. Many climate change studies have assumed advances in sowing date are often larger than likely based on historical trends and models developed in this paper. Such studies may well be overly optimistic in projected yield gains that follow from earlier sowing. Our results also revealed that sowing dates were correlated with the sum of solar radiation over the preceding 7–10 months. This correlation remained significant also when other weather variables (rain, temperature, frost days) were accounted for. Our work raises the question what is the process causing this correlation and does such correlation with radiation sum also show up in other time series of phenological events or human behaviour. [Copyright &y& Elsevier]

Abstract: Studies aimed at assessing agriculture in the context of climate change, natural resource use and food security are only useful if they account for the effect of changes in agricultural management and technological innovation. The challenge is, however, that the number of possible alternative management options and technological changes is enormous which hinders a transparent analysis. This paper presents an approach based on Data Envelopment Analysis (DEA) to identify a manageable set of representative alternative activities out of a large set of possible alternatives. The smaller set is superior in terms of different indicators that are relevant from an economic, environmental or policy point of view. The selected, superior activities can then be further analysed in a farm or natural resource use model. To demonstrate the usefulness of the method we applied it to a range of possibilities that arise if cattle manure is used instead of fertilizers in arable farms in Flevoland (the Netherlands), where due to manure surplus, arable farmers can apply manure at low costs. A large set of alternative activities (16,514) for an average arable farm in Flevoland was generated using combinatorial procedures and agronomic rules. Nutrient balances and environmental indicators were calculated at the activity level. The proposed DEA method was used to select only the superior activities in terms of a number of economic and environmental indicators. Applying DEA decreased the number of activities by 85% to 2429 activities. This smaller set of activities was then further analysed using the bio-economic optimization model Farm Systems SIMulator (FSSIM-MP). It was found that manure becomes economically interesting to farmers when transportation and application costs are low (lower than 2€/ton of manure). Comparison of the use of 16,514 activities and the 2429 activities in the same farm model resulted in exactly the same results showing that the selection method is valid. Especially when repeated calculations need to be done the selection procedure reduces the total time required for computation and facilitates the analysis of the results. The proposed method can be a complementary component for existing and future combinatorial tools that aim to identify and quantify alternative activities for policy assessment or future studies in general. [Copyright &y& Elsevier]

Abstract: To learn the historical response of cropping system to climate change will benefit the strategy decision of future cropping adaptation. In this paper, we conducted an integrated analysis of the climate records of seventy-two meteorological stations and the records of crop yields over the period 1970–2009 in Northeast China. It was found that over these forty years, the daily mean, maximum and minimum temperatures during crop growing season increased on average by 0.34°C, 0.28°C, 0.43°C every ten years, respectively. No significant change in the precipitation was found, although the differences between years were large. After de-trending the agronomic technique contributions to the increments of crop yields, the historical warming had led to great annually increments of 16.6kgha−1, 15.5kgha−1 and 3.2kgha−1 in rice, corn and soybean yields, respectively. According to the historical warming level, present cropping boundaries can be theoretically extended northward about 80km with a prolonged growing period by 10 days compared to the 1970s. Actually, the growth durations of newly approved varieties of rice, corn and soybean have really prolonged by 14.0 days, 7.0 days and 2.7 days since the 1950s, respectively. The actual growing periods of rice and corn have also respectively prolonged by 6 days and 4 days due to the adjustment of sown and harvest dates by the farmer since 1990s. The existing rice cropping region has been extended northward 80km in the 2006 compared to the 1970. These actual responses were consistent with the theoretical calculation according to the historical warming trends. Our results demonstrate that cropping system owns the potential to progressively and actively adapt to the global warming for high yield through a comprehensive strategy. [Copyright &y& Elsevier]

Rice agriculture is not only affected by climate change, but also contributes to global warming through the release of methane into the atmosphere. In 1989, a major research project was initiated at the International Rice Research Institute in the Philippines to investigate relationships between climate change and rice production. A second project started in 1993 to investigate, in more detail, mitigation options that could be employed to help reduce CH4 emissions from rice cultivation. An important component of all of this work was the quantification of these interactions between climate change and rice production into simulation models, and their subsequent use to upscale field measurements to national and regional levels. The first project developed such a model to integrate existing knowledge of effects of increased levels of CO2 and temperature on rice growth, and used this to predict the impact of various climate change scenarios on rice production in SE Asia. In the second project, routines describing the dynamics of CH4 production and emission from the soil were linked to a crop simulation model to estimate the effect of different crop management scenarios on national CH4 emissions from various countries in the region. With the recent completion of the second project, it is timely to review this modelling work describing the relationships between the global environment and rice production, a task which we attempt in the present paper. The advantages and disadvantages of the modelling approaches used and other issues relating to the upscaling of field measurements to national and regional levels are discussed. Future research directions in this area are also identified. [Copyright &y& Elsevier]

This paper reviews the knowledge on effects of climate change on agricultural productivity in Europe and the consequences for policy and research. Warming is expected to lead to a northward expansion of suitable cropping areas and a reduction of the growing period of determinate crops (e.g. cereals), but an increase for indeterminate crops (e.g. root crops). Increasing atmospheric CO2 concentrations will directly enhance plant productivity and also increase resource use efficiencies.In northern areas climate change may produce positive effects on agriculture through introduction of new crop species and varieties, higher crop production and expansion of suitable areas for crop cultivation. Disadvantages may be an increase in the need for plant protection, the risk of nutrient leaching and the turnover of soil organic matter. In southern areas the disadvantages will predominate. The possible increase in water shortage and extreme weather events may cause lower harvestable yields, higher yield variability and a reduction in suitable areas for traditional crops. These effects may reinforce the current trends of intensification of agriculture in northern and western Europe and extensification in the Mediterranean and southeastern parts of Europe.Policy will have to support the adaptation of European agriculture to climate change by encouraging the flexibility of land use, crop production, farming systems etc. In doing so, it is necessary to consider the multifunctional role of agriculture, and to strike a variable balance between economic, environmental and social functions in different European regions. Policy will also need to be concerned with agricultural strategies to mitigate climate change through a reduction in emissions of methane and nitrous oxide, an increase in carbon sequestration in agricultural soils and the growing of energy crops to substitute fossil energy use. The policies to support adaptation and mitigation to climate change will need to be linked closely to the development of agri-environmental schemes in the European Union Common Agricultural Policy.Research will have further to deal with the effect on secondary factors of agricultural production, on the quality of crop and animal production, of changes in frequency of isolated and extreme weather events on agricultural production, and the interaction with the surrounding natural ecosystems. There is also a need to study combined effects of adaptation and mitigation strategies, and include assessments of the consequences on current efforts in agricultural policy to develop a sustainable agriculture that also preserves environmental and social values in the rural society. [Copyright &y& Elsevier]

• We calibrated the DSSAT/CROPGRO for several soybean maturity groups. • Crop model showed good performance under different tropical conditions. • Water productivity increase can ensure the future growth of soybean yield. • Mean soybean yields increase likely in the future climate due to CO 2 increase. In the next decades, the population is expected to rise by more than two billion people, and the projections of climate change have been considered as one of the greatest future challenges for world food security. Soybean represents more than 60 % of all plant protein produced in the world, and Brazil is the largest world exporter and the second-largest producer. In this paper, we simulated soybean yields for 16 strategically selected agroclimatic zones (CZs) to represent Brazilian production. Experiments conducted throughout the country were used to calibrate the CROPGRO-Soybean model for Brazilian conditions, for the main maturity groups used in Brazil, to simulate current and 40 future climate scenarios, provided by Coupled Model Intercomparison Project 5 (CMIP5) for 2050 in the both 4.5 and 8.5 representative concentration pathways (RCP). We found soybean yield varying by +1 to +32 % across 16 CZs in the average scenario of future climate when compared to the current yields. Yet, we found an increase of about 5% in the yield production risk for RCP 8.5. The main reason for such results was associated with the positive effect of increasing CO 2 on crop water productivity, which overcomes the negative effects of temperature and water stress increases on rainfed Brazilian soybeans. [ABSTRACT FROM AUTHOR]

Climate change is known to impact crop yields, mainly through increased temperatures, changing rainfall patterns and increasing CO 2 concentration in the atmosphere. Although the potential effects of each of these factors have been discussed in a number of separate studies, no recent synthesis has been published to provide quantitative estimates of climate change impacts on crop yields, with or without adaptation strategies. In this paper, we synthetize a broad range of experimental or modeling studies to estimate, at the global scale, crop yield changes resulting from the marginal and combined effects of temperature, CO 2 concentration and precipitation, with and without adaptation strategies. Crop yield sensitivities are estimated by distinguishing between C3 and C4 crops. For C3 crops, our results show that the positive effects of adaptation (+7.25 %) and CO 2 (+9% for +100 ppm) are high enough to offset the negative effects of temperature increase (-2.4 % for +1 °C), even at +4 °C. On the other hand, for maize (i.e., the only C4 plant species in our database) the somewhat low positive effect from increased CO 2 concentration and the absence of a significant effect of adaptation lead to higher yield losses, in the order of -10 % for +4 °C. The minimum level of CO 2 concentration increase requested to achieve a yield gain under increased temperature conditions is much higher for maize than for C3 crops, in particular for wheat. The estimated effects of adaptation are uncertain, especially for soybean and rice, but also for maize, where the absence of a significant adaptation effect is probably at least partly due to limited data availability. Our results demonstrate that CO 2 effects on crop yields should not be overlooked in foresight studies on the impacts of climate change. Our analysis also highlights the importance of improving our knowledge of how effective adapation strategies are in mitigating the impact of climate change. [ABSTRACT FROM AUTHOR]