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Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051-2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.
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Secas , Zea mays , África do Sul , Mudança Climática , Grão Comestível , AgriculturaRESUMO
Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.
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Mudança Climática , Triticum , Biomassa , Estações do Ano , TemperaturaRESUMO
Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
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Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.
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Adaptação Fisiológica , Mudança Climática , Proteínas de Grãos/análise , Triticum/química , Triticum/fisiologia , Dióxido de Carbono/metabolismo , Secas , Qualidade dos Alimentos , Modelos Teóricos , Nitrogênio/metabolismo , TemperaturaRESUMO
Cocoa agroforestry is perceived as potential adaptation strategy to sub-optimal or adverse environmental conditions such as drought. We tested this strategy over wet, dry and extremely dry periods comparing cocoa in full sun with agroforestry systems: shaded by (i) a leguminous tree species, Albizia ferruginea and (ii) Antiaris toxicaria, the most common shade tree species in the region. We monitored micro-climate, sap flux density, throughfall, and soil water content from November 2014 to March 2016 at the forest-savannah transition zone of Ghana with climate and drought events during the study period serving as proxy for projected future climatic conditions in marginal cocoa cultivation areas of West Africa. Combined transpiration of cocoa and shade trees was significantly higher than cocoa in full sun during wet and dry periods. During wet period, transpiration rate of cocoa plants shaded by A. ferruginea was significantly lower than cocoa under A. toxicaria and full sun. During the extreme drought of 2015/16, all cocoa plants under A. ferruginea died. Cocoa plants under A. toxicaria suffered 77% mortality and massive stress with significantly reduced sap flux density of 115 g cm-2 day-1 , whereas cocoa in full sun maintained higher sap flux density of 170 g cm-2 day-1 . Moreover, cocoa sap flux recovery after the extreme drought was significantly higher in full sun (163 g cm-2 day-1 ) than under A. toxicaria (37 g cm-2 day-1 ). Soil water content in full sun was higher than in shaded systems suggesting that cocoa mortality in the shaded systems was linked to strong competition for soil water. The present results have major implications for cocoa cultivation under climate change. Promoting shade cocoa agroforestry as drought resilient system especially under climate change needs to be carefully reconsidered as shade tree species such as the recommended leguminous A. ferruginea constitute major risk to cocoa functioning under extended severe drought.
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Agricultura/métodos , Cacau/fisiologia , Mudança Climática , Florestas , Adaptação Fisiológica , Luz Solar , ÁguaRESUMO
Resilience of cocoa agroforestry vs. full sun under extreme climatic conditions. In the specific case of our study, the two shade tree species associated with cocoa resulted in strong competition for water and became a disadvantage to the cocoa plants contrary to expected positive effects.
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Árvores , ÁguaRESUMO
Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
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Mudança Climática , Produtos Agrícolas/fisiologia , Modelos Biológicos , Incerteza , Regiões Árticas , Produtos Agrícolas/crescimento & desenvolvimento , Finlândia , Previsões , Região do Mediterrâneo , Espanha , Fatores de TempoRESUMO
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
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Agricultura , Mudança Climática , Modelos Teóricos , Agricultura/métodos , Meio Ambiente , TriticumRESUMO
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
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Clima , Modelos Biológicos , Triticum/crescimento & desenvolvimento , Mudança Climática , Meio Ambiente , Estações do AnoRESUMO
Maize phenology observations at 112 national agro-meteorological experiment stations across China spanning the years 1981-2009 were used to investigate the spatiotemporal changes of maize phenology, as well as the relations to temperature change and cultivar shift. The greater scope of the dataset allows us to estimate the effects of temperature change and cultivar shift on maize phenology more precisely. We found that maize sowing date advanced significantly at 26.0% of stations mainly for spring maize in northwestern, southwestern and northeastern China, although delayed significantly at 8.0% of stations mainly in northeastern China and the North China Plain (NCP). Maize maturity date delayed significantly at 36.6% of stations mainly in the northeastern China and the NCP. As a result, duration of maize whole growing period (GPw) was prolonged significantly at 41.1% of stations, although mean temperature (Tmean) during GPw increased at 72.3% of stations, significantly at 19.6% of stations, and Tmean was negatively correlated with the duration of GPw at 92.9% of stations and significantly at 42.9% of stations. Once disentangling the effects of temperature change and cultivar shift with an approach based on accumulated thermal development unit, we found that increase in temperature advanced heading date and maturity date and reduced the duration of GPw at 81.3%, 82.1% and 83.9% of stations on average by 3.2, 6.0 and 3.5 days/decade, respectively. By contrast, cultivar shift delayed heading date and maturity date and prolonged the duration of GPw at 75.0%, 94.6% and 92.9% of stations on average by 1.5, 6.5 and 6.5 days/decade, respectively. Our results suggest that maize production is adapting to ongoing climate change by shift of sowing date and adoption of cultivars with longer growing period. The spatiotemporal changes of maize phenology presented here can further guide the development of adaptation options for maize production in near future.
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Agricultura/métodos , Mudança Climática , Modelos Biológicos , Temperatura , Zea mays/crescimento & desenvolvimento , China , Geografia , Fatores de TempoRESUMO
Lack of nitrogen limits food production in poor countries while excessive nitrogen use in industrial countries has led to transgression of the planetary boundary. However, the potential of spatial redistribution of nitrogen input for food security when returning to the safe boundary has not been quantified in a robust manner. Using an emulator of a global gridded crop model ensemble, we found that redistribution of current nitrogen input to major cereals among countries can double production in the most food-insecure countries, while increasing global production of these crops by 12% with no notable regional loss or reducing the nitrogen input to the current production by one-third. Redistribution of the input within the boundary increased production by 6-8% compared to the current relative distribution, increasing production in the food-insecure countries by two-thirds. Our findings provide georeferenced guidelines for redistributing nitrogen use to enhance food security while safeguarding the planet.
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Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions-the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers' perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers' perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.
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Agricultura/métodos , Mudança Climática , Grão Comestível/crescimento & desenvolvimento , Política Ambiental/tendências , Etiópia , Humanos , Entrevistas como Assunto , Chuva , Fatores Socioeconômicos , Inquéritos e Questionários , TemperaturaRESUMO
Introduction: Breeding barley cultivars adapted to drought requires in-depth knowledge on physiological drought responses. Methods: We used a high-throughput functional phenotyping platform to examine the response of four high-yielding European spring barley cultivars to a standardized drought treatment imposed around flowering. Results: Cv. Chanell showed a non-conserving water-use behavior with high transpiration and maximum productivity under well-watered conditions but rapid transpiration decrease under drought. The poor recovery upon re-irrigation translated to large yield losses. Cv. Baronesse showed the most water-conserving behavior, with the lowest pre-drought transpiration and the most gradual transpiration reduction under drought. Its good recovery (resilience) prevented large yield losses. Cv. Formula was less conserving than cv. Baronesse and produced low yet stable yields. Cv. RGT's dynamic water use with high transpiration under ample water supply and moderate transpiration decrease under drought combined with high resilience secured the highest and most stable yields. Discussion: Such a dynamic water-use behavior combined with higher drought resilience and favorable root traits could potentially create an ideotype for intermediate drought. Prospective studies will examine these results in field experiments and will use the newly gained understanding on water use in barley to improve process descriptions in crop simulation models to support crop model-aided ideotype design.
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Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.
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Aclimatação , Água , Estações do Ano , Adaptação Fisiológica , AgriculturaRESUMO
Articles published in Food Security in 2021 are reviewed, showing a wide range of topics covered. Many articles are directly linked with "food" and associated terms such as "nutritive", "nutrition", "dietary", and "health". Another important group is linked with (food) "production" and a range of connected terms including: "irrigation", "cultivated", "organic", "varieties", "crop", "vegetable", and "land". A third group of terms refers to the scales at which food security is considered: "household", "farmer", "farm", "smallholder", "community", "nation" and "region". A few themes of Food Security are considered: (1) food supply and demand, food prices, and global trade; (2) food security in households; (3) food production; (4) value chains and food systems; (5) the evolution of the concept of food security; and (6) global nutrition. In a last section, perspectives for Food Security are discussed along four lines of thoughts: the level of inter-disciplinary research published in Food Security; the importance of the Social Sciences for food security as a collective good underpinned by other collective goods within food systems; the balance between the Global South and the Global North in Food Security; and a warning that urgent global challenges that vitally interact with food security may be left unattended as a result of the current public health emergency.
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Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.
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This opinion article results from a collective analysis by the Editorial Board of Food Security. It is motivated by the ongoing covid-19 global epidemic, but expands to a broader view on the crises that disrupt food systems and threaten food security, locally to globally. Beyond the public health crisis it is causing, the current global pandemic is impacting food systems, locally and globally. Crises such as the present one can, and do, affect the stability of food production. One of the worst fears is the impacts that crises could have on the potential to produce food, that is, on the primary production of food itself, for example, if material and non-material infrastructure on which agriculture depends were to be damaged, weakened, or fall in disarray. Looking beyond the present, and not minimising its importance, the covid-19 crisis may turn out to be the trigger for overdue fundamental transformations of agriculture and the global food system. This is because the global food system does not work well today: the number of hungry people in the world has increased substantially, with the World Food Programme warning of the possibility of a "hunger pandemic". Food also must be nutritious, yet unhealthy diets are a leading cause of death. Deepening crises impoverish the poorest, disrupt food systems, and expand "food deserts". A focus on healthy diets for all is all the more relevant when everyone's immune system must react to infection during a global pandemic. There is also accumulating and compelling evidence that the global food system is pushing the Earth system beyond the boundaries of sustainability. In the past twenty years, the growing demand for food has increasingly been met through the destruction of Earth's natural environment, and much less through progress in agricultural productivity generated by scientific research, as was the case during the two previous decades. There is an urgent need to reduce the environmental footprint of the global food system: if its performances are not improved rapidly, the food system could itself be one main cause for food crises in the near future. The article concludes with a series of recommendations intended for policy makers and science leaders to improve the resilience of the food system, global to local, and in the short, medium and long term.
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Global warming is expected to increase the frequency and intensity of severe water scarcity (SWS) events, which negatively affect rain-fed crops such as wheat, a key source of calories and protein for humans. Here, we develop a method to simultaneously quantify SWS over the world's entire wheat-growing area and calculate the probabilities of multiple/sequential SWS events for baseline and future climates. Our projections show that, without climate change mitigation (representative concentration pathway 8.5), up to 60% of the current wheat-growing area will face simultaneous SWS events by the end of this century, compared to 15% today. Climate change stabilization in line with the Paris Agreement would substantially reduce the negative effects, but they would still double between 2041 and 2070 compared to current conditions. Future assessments of production shocks in food security should explicitly include the risk of severe, prolonged, and near-simultaneous droughts across key world wheat-producing areas.
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Mudança Climática , Produtos Agrícolas , Modelos Teóricos , Triticum , Água , Abastecimento de Alimentos , Geografia , Aquecimento Global , Humanos , Estações do AnoRESUMO
Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
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Agricultura , Produtos Agrícolas/crescimento & desenvolvimento , Temperatura , Simulação por Computador , Modelos BiológicosRESUMO
This corrects the article DOI: 10.1038/nplants.2017.102.