Search

Societal Impact Statement: As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation. Summary: There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments. Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments. Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E. We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.

The severity of the climate challenge requires a change in the climate response, from an incrementalto a more far-reaching and radical transformative one. There is also a need to avoid maladaptationwhereby responses to climate risk inadvertently reinforce vulnerability, exposure and risk for somesections of society. Innovative technological interventions are critical but enabling social,institutional and governance factors are the actual drivers of the transformative process. Bringingabout this transformation requires inter- and transdisciplinary approaches, and the embracing ofsocial equity. In this Perspective, we unpack what this means for agricultural research and, based onour collective experience, we map out a research agenda that weaves different research componentsinto a holistic and transformative one. We do not offer best practice, but rather reflections on howagricultural research can more readily contribute to transformative adaptation, along with thepersonal and practical challenges of designing and implementing such an agenda., ClimBeR

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

PR, 1 Fostering Climate-Resilient and Sustainable Food Supply; IFPRI3; ISI, CPA; EPTD, The severity of the climate challenge requires a change in the climate response, from an incremental to a more far-reaching and radical transformative one. There is also a need to avoid maladaptation whereby responses to climate risk inadvertently reinforce vulnerability, exposure and risk for some sections of society. Innovative technological interventions are critical but enabling social, institutional and governance factors are the actual drivers of the transformative process. Bringing about this transformation requires inter- and transdisciplinary approaches, and the embracing of social equity. In this Perspective, we unpack what this means for agricultural research and, based on our collective experience, we map out a research agenda that weaves different research components into a holistic and transformative one. We do not offer best practice, but rather reflections on how agricultural research can more readily contribute to transformative adaptation, along with the personal and practical challenges of designing and implementing such an agenda.

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 under

Climate change is causing problems for agriculture, but the effect of combined abiotic stresses on crop nutritional quality is not clear. Here we studied the effect of 10 combinations of climatic conditions (temperature, CO2, O3 and drought) under controlled growth chamber conditions on the grain yield, protein, and mineral content of 3 wheat varieties. Results show that wheat plants under O3 exposure alone concentrated + 15 to + 31% more grain N, Fe, Mg, Mn P and Zn, reduced K by − 5%, and C did not change. Ozone in the presence of elevated CO2 and higher temperature enhanced the content of Fe, Mn, P and Zn by 2–18%. Water-limited chronic O3 exposure resulted in + 9 to + 46% higher concentrations of all the minerals, except K. The effect of climate abiotic factors could increase the ability of wheat to meet adult daily dietary requirements by + 6% to + 12% for protein, Zn and Fe, but decrease those of Mg, Mn and P by − 3% to − 6%, and K by − 62%. The role of wheat in future nutrition security is discussed.