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Abstract Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.

The rice-maize (R-M) system is rapidly expanding in Bangladesh due to its greater suitability for diverse soil types and environments. The present conventional method of cultivating puddled transplanted rice and maize is input-intensive, decreases soil health through intense ploughing, and ultimately reduces farm profitability. There is a need to investigate alternatives. Accordingly, we conducted a replicated 2-year (2020–2021) field study to investigate the effects of conservation agriculture (CA) based tillage and crop establishment (TCE) techniques and residue management practices on the physical, chemical, and biological properties of soil along with crop productivity and the profitability of rice-maize systems in the sandy loam soil of Northwest Bangladesh. Two TCE techniques Puddled transplanted rice (PTR) followed by Conventional tillage maize (CTM) and strip tillage direct-seeded rice (STDSR) followed by strip-tilled maize (STM) were assigned to the main plots and different percentages of crop residue retention (0, 25, and 50% by height) were allocated to the subplots. Results showed that a reduction in bulk density (BD), soil penetration resistance (SPR), and increased soil porosity were associated with STDSR/STM-based scenarios (strip tillage coupled with 25 and 50% residue retention). The soil organic carbon (SOC) fractions, such as dissolved organic C (DOC), light and heavy particulate organic matter C (POM-C), MAOM, and microbial biomass C (MBC) levels in the 0–10 cm layer under ST based treatments were 95, 8, 6, 2 and 45% greater, respectively, compared to CT with no residue treatment. When compared to the CT treatment, the DOC, light POM-C, heavy POM-C, and MAOM in the 10–20 cm layer with ST treatment were 8, 34, 25, 4 and 37% higher, respectively. Residue retention in ST increased average rice, maize, and system yields by 9.2, 14.0, and 14.12%, respectively, when compared to CT. The system gross margin and benefit-cost ratio (BCR) were $1,515 ha−1 and 1.90 under conventional tillage to $1,696 ha−1 and 2.15 under strip-tillage practices. Thus, our study suggests that CA could be an appropriate practice for sustaining soil fertility and crop yield under R-M systems in light-textured soils or other similar soils in Bangladesh.

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.

Climate change will put millions more people in Africa at risk of food and nutrition insecurity by 2050. Integrated assessments of food systems tend to be limited by either heavy reliance on models or a lack of information on food and nutrition security. Accordingly, we developed a novel integrated assessment framework that combines models with in-country knowledge and expert academic judgement to explore climate-smart and nutrition-secure food system futures: the integrated Future Estimator for Emissions and Diets (iFEED). Here, we describe iFEED and present its application in Malawi, South Africa, Tanzania and Zambia. The iFEED process begins with a participatory scenario workshop. In-country stakeholders identify two key drivers of food system change, and from these, four possible scenarios are defined. These scenarios provide the underlying narratives of change to the food system. Integrated modeling of climate change, food production and greenhouse gas emissions is then used to explore nutrition security and climate-smart agriculture outcomes for each scenario. Model results are summarized using calibrated statements—quantitative statements of model outcomes and our confidence in them. These include statements about the way in which different trade futures interact with climate change and domestic production in determining nutrition security at the national level. To understand what the model results mean for food systems, the calibrated statements are expanded upon using implication statements. The implications rely on input from a wide range of academic experts—including agro-ecologists and social scientists. A series of workshops are used to incorporate in-country expertise, identifying any gaps in knowledge and summarizing information for country-level recommendations. iFEED stakeholder champions help throughout by providing in-country expertise and disseminating knowledge to policy makers. iFEED has numerous novel aspects that can be used and developed in future work. It provides information to support evidence-based decisions for a climate-smart and nutrition-secure future. In particular, iFEED: (i) employs novel and inclusive reporting of model results and associated in-country food system activities, with comprehensive reporting of uncertainty; (ii) includes climate change mitigation alongside adaptation measures; and (iii) quantifies future population-level nutrition security, as opposed to simply assessing future production and food security implications.

Abstract Over the last three decades, few studies have been conducted to tackle the complexity and heterogeneity of Bangladesh farming systems. We address these research gaps with a new survey. Accordingly, a survey was conducted in North‐Western Bangladesh to understand how socio‐economic traits influence technology adoption and to identify and characterize key farm types. The survey was based on farm household characteristics, farm structure, farming practices and livestock as well as the economic performance of the farm. Principal component analysis (PCA) and cluster analysis (CA) were used to establish the different farm typologies, and the data set based on 27 variables was carefully analysed. The findings confirmed that the key variables that significantly affect the adoption of new agricultural technologies relate to age, farming experience, level of education of the household head, income, access to markets, land ownership, the proportion of hired labour, savings, food self‐sufficiency and income from off‐farm activities. Four main farm types were identified in the study area based on resource endowment and livelihood orientation. These are (1) well‐resourced farmers entirely dependent on agriculture and less reliant on off‐farm activities; (2) moderately resourced households, which are headed by an older male with greater farming experience and which are engaged in both on‐farm and off‐farm activities; (3) resource‐constrained households with cattle as the main livestock and with income generated by the sale of livestock products; and (4) severely resource‐constrained households which are headed by young farmers/men and where income is generated by off‐farm activities. These four farm categories represent the heterogeneity of farms in North‐West Bangladesh, and it is hoped that the development of this farm household typology will help particularly the extension service, to set up appropriate extension advice that will benefit the farming community.

The contribution of potatoes to the global food supply is increasing—consumption more than doubled in developing countries between 1960 and 2005. Understanding climate change impacts on global potato yields is therefore important for future food security. Analyses of climate change impacts on potato compared to other major crops are rare, especially at the global scale. Of two global gridded potato modeling studies published at the time of this analysis, one simulated the impacts of temperature increases on potential potato yields; the other did not simulate the impacts of farmer adaptation to climate change, which may offset negative climate change impacts on yield. These studies may therefore overestimate negative climate change impacts on yields as they do not simultaneously include CO2 fertilisation and adaptation to climate change. Here we simulate the abiotic impacts of climate change on potato to 2050 using the GLAM crop model and the ISI-MIP ensemble of global climate models. Simulations include adaptations to climate change through varying planting windows and varieties and CO2 fertilisation, unlike previous global potato modeling studies. Results show significant skill in reproducing observed national scale yields in Europe. Elsewhere, correlations are generally positive but low, primarily due to poor relationships between national scale observed yields and climate. Future climate simulations including adaptation to climate change through changing planting windows and crop varieties show that yields are expected to increase in most cases as a result of longer growing seasons and CO2 fertilisation. Average global yield increases range from 9 to 20% when including adaptation. The global average yield benefits of adaptation to climate change range from 10 to 17% across climate models. Potato agriculture is associated with lower green house gas emissions relative to other major crops and therefore can be seen as a climate smart option given projected yield increases with adaptation.

Common bean is the most consumed legume in the world and an important source of protein in Latin America, Eastern, and Southern Africa. It is grown in a variety of environments with mean air temperatures of between 14°C and 35°C and is more sensitive to high temperatures than other legumes. As global heating continues, breeding for heat tolerance in common bean is an urgent priority. Transpirational cooling has been shown to be an important mechanism for heat avoidance in many crops, and leaf cooling traits have been used to breed for both drought and heat tolerance. As yet, little is known about the magnitude of leaf cooling in common bean, nor whether this trait is functionally linked to heat tolerance. Accordingly, we explore the extent and genotypic variation of transpirational cooling in common bean. Our results show that leaf cooling is an important heat avoidance mechanism in common bean. On average, leaf temperatures are 5°C cooler than air temperatures, and can range from between 13°C cooler and 2°C warmer. We show that the magnitude of leaf cooling keeps leaf temperatures within a photosynthetically functional range. Heat tolerant genotypes cool more than heat sensitive genotypes and the magnitude of this difference increases at elevated temperatures. Furthermore, we find that differences in leaf cooling are largest at the top of the canopy where determinate bush beans are most sensitive to the impact of high temperatures during the flowering period. Our results suggest that heat tolerant genotypes cool more than heat sensitive genotypes as a result of higher stomatal conductance and enhanced transpirational cooling. We demonstrate that it is possible to accurately simulate the temperature of the leaf by genotype using only air temperature and relative humidity. Our work suggests that greater leaf cooling is a pathway to heat tolerance. Bean breeders can use the difference between air and leaf temperature to screen for genotypes with enhanced capacity for heat avoidance. Once evaluated for a particular target population of environments, breeders can use our model for modeling leaf temperatures by genotype to assess the value of selecting for cooler beans.

Efforts to increase agricultural productivity, adapt to climate change, and reduce the carbon footprint of agriculture are reflected in a growing interest in climate-smart agriculture (CSA). Specific indicators of productivity, adaptation and mitigation are commonly used in support of claims about the climate smartness of practices. However, it is rare that these three objectives can be optimized simultaneously by any one strategy. In evaluating the relative climate smartness of different agricultural practices, plans and policies, there is a need for metrics that can simultaneously represent all three objectives and therefore be used in comparing strategies that have different benefits and trade-offs across this triad of objectives. In this context, a method for developing a Climate Smartness Index (CSI) is presented. The process of developing the index follows four steps: (1) defining system specific climate smartness; (2) selecting relevant indicators; (3) normalizing against reference values from a systematic literature review; and (4) aggregating and weighting. The CSI presented here has been developed for application in a systematic review of rice irrigation strategies and it combines normalized water productivity (WP) and greenhouse gas intensity (GHGI) The CSI was developed for application to data from published field experiments that assessed the impact of water management practices in irrigated rice, focusing on practices heralded as climate-smart strategies, such as Alternate Wetting and Drying (AWD). The analysis shows that the CSI can provide a consistent judgment of the treatments based on the evidence of water efficiency and reduced GHGI reported in such studies. Using a measurable and replicable index supports the aim of generating a reliable quantification of the climate smartness of agricultural practices. The same four step process can be used to build metrics for a broad range of CSA practice, policy and planning.

Studies of climate impacts on agriculture and adaptation often provide current or future assessments, ignoring the historical contexts farming systems are situated within. We investigate how historical trends have influenced farming system adaptive capacity in Uganda using data from household surveys, semi-structured interviews, focus-group discussions and observations. By comparing two farming systems, we note three major findings: (1) similar trends in farming system evolution have had differential impacts on the diversity of farming systems; (2) trends have contributed to the erosion of informal social and cultural institutions and an increasing dependence on formal institutions; and (3) trade-offs between components of adaptive capacity are made at the farm-scale, thus influencing farming system adaptive capacity. To identify the actual impacts of future climate change and variability, it is important to recognize the dynamic nature of adaptation. In practice, areas identified for further adaptation support include: shift away from one-size-fits-all approach the identification and integration of appropriate modern farming method; a greater focus on building inclusive formal and informal institutions; and a more nuanced understanding regarding the roles and decision-making processes of influential, but external, actors. More research is needed to understand farm-scale trade-offs and the resulting impacts across spatial and temporal scales.

Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18 ° C (2–130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5–15% for climatological mean temperatures, 3–5% for diurnal range and 1–2% in precipitation. At these improvement rates, we estimate that at least 5–30 years of CMIP work is required to improve regional temperature simulations and at least 30–50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies.

As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions.