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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]

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]

• 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]