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Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments
Abstract
Context: Accurately projecting crop yields under climate change is essential for understanding potential impacts
and planning of agricultural adaptation in sub-Saharan Africa (SSA). Crop growth models and machine learning
(ML) are often used, but their effectiveness is limited by data availability, precision, and geographic coverage in
SSA.
Objective: This study aimed to integrate ML with a process-based crop model to produce geographically
continuous gridded crop yield projections while reducing uncertainties associated with standalone ML or crop
growth models. As a case study, we implemented it to project the climate change impact on water-limited po�tential yield of maize across SSA.
Methods: We developed an integrated system that combines ML with eco-physiological processes to estimate
sowing dates and thermal times, ensuring that crop phenology is accounted for, thus improving potential rainfed
yield simulations under varying environmental conditions. Random Forest and crop model-based algorithms are
integrated in three steps: (i) RF1, a Random Forest model integrated with a sowing algorithm, designed to es�timate the sowing window and sowing date; (ii) RF2, a Random Forest model combined with a crop model algorithm to estimate cumulative thermal time during the growing season, used to determine the timing of
phenological stages; and (iii) RF3, another Random Forest model, trained based on eco-physiological principles
applied in phases (i) and (ii), employed to simulate water-limited potential yield. The outcomes of the different
steps of the framework under historical conditions were tested against reported data across SSA.
Results and conclusions: For maize and historical climatic conditions, the framework delivers yields which differ
less than 20 % of those simulated with a crop model with high-quality inputs, in 95 % of the cases. Our approach
thus shows value for generating crop yield projections in data-scarce regions under historical climate, and under
future climatic conditions which already feature today somewhere in SSA and for which the framework has been
trained.
Significance: Our approach can also be applied to other major food crops in SSA, under both current and climate
change conditions. It allows testing the effect of adaptation of crop cultivars in terms of maturity group. Thus, it
can be used for different crops and with far less data requirements compared to process-based crop models. It has
the potential for significant applications in assessing climate change impacts, guiding adaptation strategies, and
supporting crop breeding programmes and policymaking efforts in SSA.
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Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km² at the Equator) and accuracy for maize, wheat and rice. These maps serve as a robust reference to benchmark farmers’ yields in the context of current cropping systems and water regimes and can help to identify areas with large room to increase crop yields.
The escalating climate instability and extreme weather events significantly jeopardize food security. The study assessed the impact of long-term climatic variables and extreme weather events on soybean and wheat yields in rainfed central India. To address inherent spatial variability, the study area was divided into homogeneous zones based on rainfall and soil parameters. Crop yields were correlated with a comprehensive set of driving variables at seasonal and monthly scales within each zone. Machine learning algorithms, including Random Forest Regression (RFR) and Neural Networks (NN), were employed to analyze crop yield anomalies caused by climate and weather extremes. The Sobol’ index was utilized for global sensitivity analysis to identify key parameters. Results showed significant negative correlations between thermo-meteorological parameters and yields of both monsoon soybean and winter wheat across multiple districts. Soybean yield exhibited a notable positive correlation with hydro-meteorological parameters, while wheat yield displayed a significant positive correlation with cold temperature extremes. RFR and NN demonstrated similar performance, with Root Mean Square Error (RMSE) values ranging from 0.27 to 0.39 t/ha for soybean and 0.4 to 0.6 t/ha for wheat. The Sobol’ index highlighted the high sensitivity of soybean yield to rainfall and rainy days during July and August, corresponding to the crop development and flowering stages. In contrast, wheat yield was primarily influenced by temperature extremes, particularly cold nights and hot days during the reproductive-maturity stage. These crop- and growth-stage-specific analyses of meteorological parameters are essential for devising effective strategies to adapt and mitigate climate emergencies.
Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies –WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995–2014), 2030 (2020–2039), and 2050 (2040–2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes.
In Ghana, most smallholder maize farmers delay harvesting of their crops in an attempt to achieve optimum moisture content levels necessary for safe storage. Late harvesting may cause a hike in insect attacks and fungal contaminations, leading to alterations in nutrient composition of grains. This study examined the effects of harvest time and storage form on quality of maize. Maize was grown and harvested from 36 plots, with each plot measuring 3m x 3m. Planting of maize was done during the major and minor seasons (April – August, 2020 and September – December, 2020) respectively. Moisture content of maize before storage was determined as 12.50 % to 12.85 % (major season) and 11.90 % to 12.48% (minor season). Harvesting was done at three stages (E = Early harvest, M = Mid harvest and L = Late harvest) and maize was stored for 90 days in three different ways (D = Dehusked, H = Husked and S = Shelled). Data was subjected to Analysis of variance (ANOVA) using Sisvar version 5.6. Mid harvest dehusked maize had the highest final starch content (69.28 %) while Early harvest husked maize had the highest protein content (7.22 %). Ash content of maize from the various treatments ranged from 3.50% to 5.39 % (initial) and 3.03 % to 4.13 % (final), the difference was significant (p<0.05). Late harvest husked maize (LHH) recorded 35 % more initial ash as compared to EHS. Aflatoxin level was highest on Late harvest dehusked maize (60.70 ppb). Nutrient and aflatoxin levels of maize were significantly affected by harvest time. Encouraging farmers to adopt a better approach to harvesting, drying and storage of maize can reduce crop losses and ensure food security.
Common bean (Phaseolus vulgaris L.) is the second most important source of dietary protein and the third most important source of calories in Africa, especially for the poor. In East Africa, drought is an important constraint to bean production. Therefore, breeding programs in East Africa have been trying to develop drought resistant varieties of common bean. To do this, breeders need information about seasonal drought stress patterns including their onset, intensity, and duration in the target area of the breeding program, so that they can mimic this pattern during field trials. Using the Decision Support for Agrotechnology Transfer (DSSAT) v4.7 model together with historical and future (Coupled Model Inter-comparison Project 6, CMIP6) climate data, this study categorized Ethiopia, Tanzania, and Uganda into different target population of environments (TPEs) based on historical and future seasonal drought stress patterns. We find that stress-free conditions generally dominate across the three countries under historical conditions (50-80% frequency). These conditions are projected to increase in frequency in Ethiopia by 2-10% but the converse is true for Tanzania (2-8% reduction) and Uganda (17-20% reduction) by 2050 depending on the Shared Socioeconomic Pathway (SSP). Accordingly, by 2050, terminal drought stresses of various intensities (moderate, severe, extreme) are prevalent in 34% of Uganda, around a quarter of Ethiopia, and 40% of the bean growing environments in Tanzania. The TPEs identified in each country serve as a basis for prioritizing breeding activities in national programs. However, to optimize resource use in international breeding programs to develop genotypes that are resilient to future projected stress patterns, we argue that common bean breeding programs should focus primarily on identifying genotypes with tolerance to severe terminal drought, with co-benefits in relation to adaptation to moderate and extreme terminal drought. Little to no emphasis on heat stress is warranted by 2050s.
Drought estimates in terms of physically measurable variables such as precipitation deficit or streamflow deficit are key knowledge for an effective water management. How these deficits vary with the drought event severity indicated by commonly used standardized indices is often unclear. Drought severity calculated from the drought index does not necessarily correspond to the same amount of deficit in precipitation or streamflow at different regions, and it is different for each month in the same region. We investigate drought to remove this disadvantage of the index-based drought intensity–duration–frequency (IDF) curves and develop IDF curves in terms of the associated deficit. In order to study the variation of deficits, we use the link between precipitation and streamflow and the associated indices, the Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSI). More specifically, the analysis relies on frequency analysis combined with the total probability theorem applied to the critical drought severity. The critical drought has varying durations, and it is extracted from dry periods. IDF curves in terms of precipitation and streamflow deficits for the most severe drought of each drought duration in each year are then subject to comparison of statistical characteristics of droughts for different return periods. Precipitation and streamflow data from two catchments, the Seyhan River (Türkiye) and the Kocher River (Germany), provide examples for two climatically and hydrologically different cases. A comparison of the two cases allows a similar method to be tested in different hydrological conditions. We found that precipitation and streamflow deficits vary systematically, reflecting seasonality and the magnitude of precipitation and streamflow characteristics of the catchments. Deficits change from one month to another at a given station. Higher precipitation deficits were observed in winter months compared to summer months. Additionally, we assessed observed past major droughts experienced in both catchments on the IDF curves, which show that the major droughts have return periods at the order of 100 years at short durations. This coincides with the observation in the catchments and shows the applicability of the IDF curves. The IDF curves can be considered a tool for using in a range of specific activities of agriculture, ecology, industry, energy and water supply, etc. This is particularly important to end users and decision-makers to act against the drought quickly and precisely in a more physically understandable manner.
Crop yields are increasingly affected by climate change-induced weather extremes in Germany. However, there is still little knowledge of the specific crop-climate relations and respective heat and drought stress-induced yield losses. Therefore, we configure weather indices (WIs) that differ in the timing and intensity of heat and drought stress in wheat (Triticum aestivum L.). We construct these WIs using gridded weather and phenology time series data from 1995 to 2019 and aggregate them with Germany-wide municipality level on-farm wheat yield data. We statistically analyze the WI’s explanatory power and region-specific effect size for wheat yield using linear mixed models. We found the highest explanatory power during the stem elongation and booting phase under moderate drought stress and during the reproductive phase under moderate heat stress. Furthermore, we observed the highest average yield losses due to moderate and extreme heat stress during the reproductive phase. The highest heat and drought stress-induced yield losses were observed in Brandenburg, Saxony-Anhalt, and northern Bavaria, while similar heat and drought stresses cause much lower yield losses in other regions of Germany.
Introduction: Access to high-quality seeds remains a key constraint to the intensification of crop production in low-income countries. In this study, we analyzed maize seed production and distribution systems in Benin to identify leverage points for effective seed systems, a prerequisite for improving maize production.
Methods: Semi-structured interviews were conducted with 81 seed producers selected in seven municipalities across the three phytogeographical zones of Benin. Key informant interviews were also conducted with ten public and private stakeholders involved in maize seed systems in Benin.
Results and discussion: Findings showed that the legal and institutional frameworks governing seed systems in Benin were recently reinforced with a national seed policy, the creation and operationalization of the National Committee of Plant Seeds and the existence of regulations and rules on the production, quality control, certification, trade, and packaging of seeds. In addition, enabling conditions to facilitate the involvement of the private sector have been greatly improved with the revision of modalities for obtaining approval for the production and distribution of seeds in Benin. While the seed sector is improving and both public and private stakeholders are involved in maize seed production and distribution, synergies among stakeholders need to be strengthened. Strengthening business and marketing skills of seed producers through training and promoting the comparative advantages of improved seeds in increasing yield and production among maize farmers could be a promising avenue. Connecting seed producers with maize farmers' organizations coupled with ICT-based agro-advisories could boost the development of the maize seed sector, and ultimately the maize value chain.
The recent wave of published global maps of ecological variables has caused as much excitement as it has received criticism. Here we look into the data and methods mostly used for creating these maps, and discuss whether the quality of predicted values can be assessed, globally and locally.
Agriculture in West Africa faces the challenge of meeting the rising demand for food as national incomes and populations increase while production becomes more uncertain due to climate change. Crop production models can provide helpful information on agricultural yields under a range of climate change scenarios and on the impact of adaptation strategies. Here, we report a systematic review of the impact of climate change on the yield of major staple crops in West Africa. Unlike earlier reviews we pay particular attention to the potential of common agricultural adaptation strategies (such as optimised planting dates, use of fertilisers and climate-resilient crop varieties) to mitigate the effects of climate change on crop yields. We systematically searched two databases for literature published between 2005 and 2020 and identified 35 relevant studies. We analysed yield changes of major staple crops (maize, sorghum, rice, millet, yam, cassava and groundnuts) caused by different climate change and field management scenarios. Yields declined by a median of 6% (-8% to +2% depending on the crop) due to climate change in all scenarios analysed. We show that the common adaptation strategies could increase crop yields affected by climate change by 13% (-4% to +19% depending on the strategy) as compared to business-as-usual field management practices, and that optimised planting dates and cultivars with longer crop cycle duration could in fact offset the negative effects of climate change on crop yields. Increased fertiliser use has not mitigated the impact of climate change on crops but could substantially increase yields now and in the future. Our results suggest that a combination of increased fertiliser use and adopting cropping practices that take advantage of favourable climate conditions have great potential to protect and enhance future crop production in West Africa.
Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.
Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated. Climate change affects agricultural productivity. New systematic global agricultural yield projections of the major crops were conducted using ensembles of the latest generation of crop and climate models. Substantial shifts in global crop productivity due to climate change will occur within the next 20 years—several decades sooner than previous projections—highlighting the need for targeted food system adaptation and risk management in the coming decades.
Food security interventions and policies need reliable estimates of crop production and the scope to enhance production on existing cropland. Here we assess the performance of two widely used ‘top-down’ gridded frameworks (Global Agro-ecological Zones and Agricultural Model Intercomparison and Improvement Project) versus an alternative ‘bottom-up’ approach (Global Yield Gap Atlas). The Global Yield Gap Atlas estimates extra production potential locally for a number of sites representing major breadbaskets and then upscales the results to larger spatial scales. We find that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current farm production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example for sub-Saharan Africa, where using different approaches would lead to different prognoses about future cereal self-sufficiency. Our study shows that foresight about food security and associated agriculture research priority setting based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches.
Recently, yield shocks due to extreme weather events and their consequences for food security have become a major concern. Although long yield time series are available in Europe, few studies have been conducted to analyze them in order to investigate the impact of adverse climate events on yield shocks under current and future climate conditions. Here we designated the lowest 10th percentile of the relative yield anomaly as yield shock and analyzed subnational wheat yield shocks across Europe during the last four decades. We applied a data‐driven attribution framework to quantify primary climate drivers of wheat yield shock probability based on machine learning and game theory, and used this framework to infer the most critical climate variables that will contribute to yield shocks in the future, under two climate change scenarios. During the period 1980–2018, our attribution analysis showed that 32% of the observed wheat yield shocks were primarily driven by water limitation, making it the leading climate driver. Projection to future climate scenarios RCP4.5 and RCP8.5 suggested an increased risk of yield shock and a paradigm shift from water limitation dominated yield shock to extreme warming induced shocks over 2070–2099: 46% and 54% of areas were primarily driven by extreme warming under RCP4.5 and RCP8.5, respectively. A similar analysis conducted on yields simulated by an ensemble of crop models showed that models can capture the negative impact of low water supply but missed the impact of excess water. These discrepancies between observed and simulated yield data call for improvement in crop models.
Climate is critically affected by aerosols, which alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud property datasets from MODIS (Moderate Resolution Imaging Spectroradiometer), onboard the Aqua satellite, and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on low-level warm-cloud microphysics and drizzle over northern Taiwan during multiple fall seasons (from 15 October to 30 November of 2005–2017). Our results indicated that northwestern Taiwan, which has several densely populated cities, is dominated by low-level clouds (e.g., warm, thin, and broken clouds) during the fall season. The observed effects of aerosols on warm clouds indicated aerosol indirect effects (i.e., increased aerosol loading caused a decrease in cloud effective radius (CER)), an increase in cloud optical thickness, an increase in cloud fraction, and a decrease in cloud-top temperature under a fixed cloud water path. Quantitatively, aerosol–cloud interactions (ACI=-∂lnCER∂lnα|CWP, changes in CER relative to changes in aerosol amounts) were 0.07 for our research domain and varied between 0.09 and 0.06 in the surrounding remote (i.e., ocean) and polluted (i.e., land) areas, respectively, indicating aerosol indirect effects were stronger in the remote area. From the raindrop size distribution analysis, high aerosol loading resulted in a decreased frequency of drizzle events, redistribution of cloud water to more numerous and smaller droplets, and reduced collision–coalescence rates. However, during light rain (≤1 mm h-1), high aerosol concentrations drove raindrops towards smaller droplet sizes and increased the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on clouds and precipitation, and observational strategies for future research on aerosol–cloud–precipitation interactions.
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
Growth and development of cereal crops are linked to weather, day length and growing degreedays (GDDs) which make them responsive to the specific environments in specific seasons. Global temperature is rising due to human activities such as burning of fossil fuels and clearance of woodlands for building construction. The rise in temperature disrupts crop growth and development. Disturbance mainly causes a shift in phenological development of crops and affects their economic yield. Scientists and farmers adapt to these phenological shifts, in part, by changing sowing time and cultivar shifts which may increase or decrease crop growth duration. Nonetheless, climate warming is a global phenomenon and cannot be avoided. In this scenario, food security can be ensured by improving cereal production through agronomic management, breeding of climate-adapted genotypes and increasing genetic biodiversity. In this review, climate warming, its impact and consequences are discussed with reference to their influences on phenological shifts. How different cereal crops adapt to climate warming by regulating their phenological development is also discussed. Based on the abovementioned discussion, different management strategies to cope with climate warming are suggested.
Satellite‐based evaluations of change in vegetation phenology have been explored extensively but land cover‐specific climate factors driving these anomalous changes are not fully understood in northern Sub‐Saharan Africa. In this study, we identified the climatic factors controlling the start of the season (SOS) extracted from GIMMS NDVI from 1988 to 2013 with onset of rainy season (ORS), annual mean temperature (Temp) and precipitation (PP) through the stepwise regression analysis. The results showed that the SOS shifted towards a late onset in a northward direction with distinct earlier and later trends in grassland and cropland, respectively. The stepwise regression has successfully built a model between SOS and its drivers in 46.0% of the total pixels, where its primary factor differed regionally across land covers. The ORS explained the local anomalous SOS change primarily at 44.7% of the pixels where the model was built. Although the ORS was the primary dominant factor in savannah and cropland, the Temp and PP were leading in grassland and shrubland, respectively, and all factors contributed evenly in evergreen forest. The difference of land cover‐specific primary factor implicates complex process in dependency of local vegetation phenology on physiological traits and climate regime across land cover in Sub‐Saharan Africa.
Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
This study investigates the impacts of climate change in Sub-Saharan Africa on yields of maize and cereals (sorghum and millet) which are of particular importance to smallholding farmers. An ensemble of six climate scenarios are input to a crop model to compute changes in food availability (mean annual yield) and food stability (standard deviation and coefficient of variation) between the periods 1971–2000 and 2041-2070. Our results show a particular risk to food security in Central Africa where mean maize yield decreases over 89% of harvested maize areas, and its variability increases over 54% of these areas. A decline in mean maize yield is computed over 85% of harvested maize areas in West Africa, 29% in Southern Africa and 32% in East Africa. Within the limits of the analysis, we find that mean yields of tropical cereals will be more robust to climate change than maize, although yields still decrease over 23% of tropical cereal harvested areas. We find declining food stability over 37% of harvested maize areas and 46% of harvested tropical cereal areas. Our findings also indicate that a range of options including regional markets, strategic food reserves, and new cultivars could help farmers adapt to these changes.
Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.
Nutrient limitation is a major constraint in crop production in sub-Saharan Africa (SSA). Here, we propose a generic and simple equilibrium model to estimate minimum input requirements of nitrogen, phosphorus and potassium for target yields in cereal crops under highly efficient management. The model was combined with Global Yield Gap Atlas data to explore minimum input requirements for self-sufficiency in 2050 for maize in nine countries in SSA. We estimate that yields have to increase from the current ca. 20% of water-limited yield potential to approximately 50–75% of the potential depending on the scenario investigated. Minimum nutrient input requirements must rise disproportionately more, with N input increasing 9-fold or 15-fold, because current production largely relies on soil nutrient mining, which cannot be sustained into the future.
This study sought to improve general understanding on rainfall timing in Nigeria. Observed monthly rainfall data for 38 meteorological stations, spanning 30 years was utilized, and the dates of onset and cessation of rainfall were estimated using Walter’s method. The interrelationships among the variables and the dependency of the amount of rainfall were calculated using Pearson’s moment correlation coefficient and multiple regression, respectively. The variance of the Standardized Anomaly Index (SAI) was used as a measure of spatial coherence. The study revealed divergent characteristics between the onset and the cessation of rainfall; progressing in opposite direction, with a very high latitudinal variation in each. Rainfall onset has a relatively high interannual variability, with an average CV of 21%, compared to 3.9% for cessation. Onset also has a larger spatial range of 137 days, compared to 82 days for cessation. Additionally, rainfall cessation was found to be more spatially coherent than the onset, but both revealed a weak spatial coherence. The relative spatial incoherence and high interannual variability of onset contrive to make it much less predictable than cessation. Interestingly, rainfall amount shows greater association with, and dependency on, onset, cessation, and rainy season length in stations of the extreme north, than in southern stations.
Maps of abiotic stresses for rice can be useful for (1) prioritizing research and (2) identifying stress hotspots, for directing technologies and varieties to those areas where they are most needed. Large-scale maps of stresses are not available for Africa. This paper considers four abiotic stresses relevant for rice in Africa (drought, cold, iron toxicity and salinity/sodicity). Maps showing hotspots of the stresses, the countries most affected and total potentially affected area are presented. In terms of relative importance, the study identified drought as the most important stress (33% of rice area potentially affected), followed by iron toxicity (12%) and then cold (7%) and salinity/sodicity (2%). Hotspots for iron toxicity, cold and salinity are identified. For drought, local variation along the hydromorphic zone was a stronger determinant than larger-scale climatic variation, therefore mapping of drought based on climatic zones has only limited value. Uncertainties in the mappings are discussed.
In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled geo-referenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km². The mean RZ-PAWHC for SSA is 74 mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km³) due to soil conditions restricting root zone depth. Of these, 4800 km³ are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km²), and 2500 km³ due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km³, aluminium toxicity by 600 km³, porosity by 120 km³ and alkalinity by 20 km³. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available.
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. This article is protected by copyright. All rights reserved.
We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.
Significance
Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is critical if policymakers, agriculturalists, and crop breeders are to ensure global food security. Our study, by compiling extensive published results from four analytical methods, shows that independent methods consistently estimated negative temperature impacts on yields of four major crops at the global scale, generally underpinned by similar impacts at country and site scales. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops, with important implications for developing crop- and region-specific adaptation strategies to ensure future food supply of an increasing world population.
Background
Maize is the most important cereal and most widely cultivated staple that plays a key role in the food security of sub-Saharan Africa (SSA). Although some countries have achieved significant gains in maize productivity, the SSA average yields are far below what could be obtained with improved cultivars under good crop management. Low cultivar turnover is one among many contributing factors to low maize yields in SSA. At present, there is a critical knowledge gap on the identity, number, and age of maize cultivars currently grown by smallholder farmers on the continent. ResultsThis study revealed that nearly 500 maize cultivars were grown in 13 African countries surveyed in the 2013/2014 main crop season. Sixty-nine percent of the cultivars each occupied <1% of the total maize area; only two cultivars occupied >40% and four occupied >30% area. Approximately 32% of all the cultivars were hybrids, 23% were improved open-pollinated varieties (OPVs), and 46% were locals. Eastern Africa (EA) and southern Africa (SA) accounted for about 43 and 38%, respectively, of all the cultivars reported, whereas West Africa’s (WA) share was 19%. The average area planted to modern cultivars in the surveyed areas was estimated at 57%—with EA, SA, and WA estimates of 82, 55, and 36%, respectively; however, increased adoption was not necessarily always related to improved productivity, as the latter depends on many additional factors. Each household planted an average of 1.781 cultivars (range 1–8). The overall weighted average age of the cultivars was 15 years, with hybrids and OPVs being 13 and 18 years, respectively. Conclusions
Maize variety turnover in SSA is slower than what is practiced in the USA and other world regions such as Latin America and Asia. The substantial variations among regions and countries in all parameters measured suggest a tailored approach to mitigation interventions. Findings of this current study pave the way for replacing the old cultivars with more recent releases that are tolerant or resistant to multiple stresses and are more resilient.
By the year 2050, the world’s population will need 60% more food than it did in 2005. In sub-Saharan Africa (we’ll call it SSA) (Fig. 1) this problem will be even greater, with the demand for cereals increasing by more than three times as the population rises.
We collected and calculated farming data for 10 countries in sub-Saharan Africa. This made us realize that countries in SSA must make many large changes to ncrease their yield of cereals (the amount of cereals that are grown on the current farmland each year) to meet this greater demand.
If countries in SSA are unable to increase cereal yield, there are two options. either farmland areas will have to increase drastically, at the expense of natural land, or SSA will need to buy more cereal from other countries than it does today. This may put more people in these countries at risk of not having enough food to be able to live healthily.
Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
Accurate estimation of yield gaps is only possible for locations where high quality local data are available, which are, however, lacking in many regions of the world. The challenge is how yield gap estimates based on location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence, insight about the minimum number of locations required to achieve robust estimates of yield gaps at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure of the performance of the climate zonation scheme for upscaling of yield potentials.
Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper (Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scaling from location-specific estimates to regional and national spatial scales.
Significance
Agriculture is arguably the sector most affected by climate change, but assessments differ and are thus difficult to compare. We provide a globally consistent, protocol-based, multimodel climate change assessment for major crops with explicit characterization of uncertainty. Results with multimodel agreement indicate strong negative effects from climate change, especially at higher levels of warming and at low latitudes where developing countries are concentrated. Simulations that consider explicit nitrogen stress result in much more severe impacts from climate change, with implications for adaptation planning.
Sub-Saharan Africa’s (SSA) demand for cereals is projected to more than double by 2050. Climate change is
generally assumed to add to the future challenges of the needed productivity increase. This study aimed to assess
(i) the potential climate change impact on four key rainfed cereals (maize, millet, sorghum and wheat) in ten SSA
countries namely Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia
using local data and national expertise, and (ii) the potential of cultivar adaptation to climate change for the four
crops. We assessed effects on rainfed potential cereal yields per crop and aggregated these to regional level in
West (WA), East and Southern Africa (ESA). We made use of a rigorous agronomic dataset for 120 locations in the
ten countries and performed simulations of rainfed potential yield (Yw) using bias-corrected climate data from
five GCMs, three time periods (1995–2014 as baseline, 2040–2059, and 2080–2099) and two scenarios
(SSP3–7.0 as business as usual and SSP5–8.5 as pessimistic). We tested whether better adapted cultivars (taken
from the pool of cultivars currently employed in the ten countries) could compensate for climate change. Results
showed that climate change decreased aggregated Yw of cereals by around 6% in ESA by 2050, whereas projected
impacts in WA were not significant. In 2090, however, the projected impact of climate change in both WA
( 24%) and ESA ( 9%). was significant. Cultivar adaptation partially compensated the negative impact of
climate change. With the adaptation approach, 87% and 82% of potential production in ESA was estimated to
occur with higher average Yw and lower variability in, respectively, 2050 and 2090, compared to the baseline
period. In WA 67% and 43% of the potential production was estimated to experience such positive effects in 2050
and 2090, respectively. These results highlight remaining adaptation challenges for 13% (2050) and 18% (2090)
in ESA and 33% (2050) and 57% (2090) in WA for potential production. In the context of the large yield gaps in
SSA, this is likely to further increase challenges to meet cereal self-sufficiency for SSA, especially in WA.
Assessing the impact of climate extremes on crop production is an important prerequisite for exploring agro-nomic practices to deal with changing climate. Process-based crop models are effective tools to assess the effect of climate change on crop yield, but cannot accurately express the impact of extreme climate events on crop yield. In this study, we developed a series of hybrid models by incorporating the APSIM model outputs and the most informative growth stage-specific extreme climate indices (ECIs) selected by two feature selection techniques (stepwise regression, SR and genetic algorithm, GA) into two machine learning algorithms (random forest, RF and light gradient boosting machine, LGBM) to evaluate impacts of climate extremes on wheat yields in the North China Plain (NCP). The results showed that the RF model outperformed the LGBM model in estimating wheat yield regardless of input variables. Applying feature selection to two machine learning algorithms can greatly reduce computational cost without significantly affecting model accuracy. The APSIM+RF-GA hybrid model was the optimal model for estimating wheat yield with explained 93 % of the observed yield variation and the accuracy of the model is improved by 33 % compared with the APSIM model alone. Extreme low temperature events before flowering and extreme high temperature events after flowering are the main extreme climate events causing the loss of wheat yield. In addition, we evaluated the impact of future climate change on wheat yield using the APSIM+RF-GA hybrid model and the APSIM model, respectively. Yields projected using a single APSIM model increased at all stations but yields projected using APSIM+RF-GA model decreased at 12.5-28.1 % of stations in the NCP under future climate scenarios. Compared to the APSIM+RF hybrid model, the future yield projected using single APSIM model might be overestimated by 12.7-19.2 % because of underestimating the yield loss caused by climate extremes. The increase of heat stress after flowering and frost stress during floral initiation to flowering were the main factors for future yield loss. Using the machine learning algorithm to make an external modification to the outputs of the APSIM model could improve the accuracy of yield estimation under extreme climate conditions and the method is more suitable for projecting future crop yield. This study is conducive to developing adaptation strategies to alleviate the negative impacts of future climate extremes on crop production.
As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.
Robust crop yield projections under future climates are fundamental prerequisites for reliable policy formation. Both process-based crop models and statistical models are commonly used for this purpose. Process-based models tend to simplify processes, minimize effects of extreme events, and ignore biotic pressures, while statistic al models cannot deterministically capture intricate biological and physiological processes underpinning crop growth. We attempted to integrate and overcome shortcomings in both modelling frameworks by integrating dynamic linear model (DLM) and random forest machine learning model (RF) with nine global gridded crop models (GGCM), respectively, in order to improve projections and reduce uncertainties of maize (Zea mays L.) and soybean (Glycine max [L.] Merrill) yield projections. Our results demonstrated substantial improvements in model performance accuracy by using RF in concert with GGCM across China’s maize and soybean belt. This improvement surpasses that achieved using DLM. For maize, the GGCM+RF models increased the r values from 0.15–0.61 to 0.64–0.77 and decreased nRMSE from approximately 0.20–0.50 to 0.13–0.17 compared with using GGCM alone. For soybean, the models increased r from 0.37–0.70 to 0.54–0.70 and decreased nRMSE from 0.17–0.35 to 0.17–0.20 compared with using GGCM alone. The main factors influencing maize yield changes included chilling days (CD), crop pests and diseases (CPDs), and drought, while for soybean the primary influencing factors included CPD, tropical days (based on exceeding a maximum temperature), and drought. Our approach decreased uncertainties by 33–78% for maize and by 56–68% for soybean . The main source of uncertainty for GGCM was the crop model. For GGCM+RF, the main source of uncertainty for the 2040–2069 period was the global climate model, while the main source of uncertainty for the 2070–2099 period was the climate scenario. Our results provide a novel, robust, and pragmatic framework to constrain uncertainties in order to accurately assess the impact of future climate change on crop yields. These results could be used to interpret future ensemble studies by accounting for uncertainty in crop and climate models, as well as to assess future emissions scenarios.
Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.
Understanding the dynamics and physics of climate extremes will be a critical challenge for twenty-first-century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts, and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016, the number of observations in the University of East Anglia Climatic Research Unit (CRU) T max product declined precipitously (5900 → 1000); 1000 poorly distributed measurements are insufficient to resolve regional T max variations. Here, we show that combining long (1983 to the near present), high-resolution (0.05°), cloud-screened archives of geostationary satellite thermal infrared (TIR) observations with a dense set of ~15 000 station observations explains 23%, 40%, 30%, 41%, and 1% more variance than the CRU globally and for South America, Africa, India, and areas north of 50°N, respectively; even greater levels of improvement are shown for the 2011–16 period (28%, 45%, 39%, 52%, and 28%, respectively). Described here for the first time, the TIR T max algorithm uses subdaily TIR distributions to screen out cloud-contaminated observations, providing accurate (correlation ≈0.8) gridded emission T max estimates. Blending these gridded fields with ~15 000 station observations provides a seamless, high-resolution source of accurate T max estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicate that the satellite-only, station-only, and combined products all perform accurately ( R ≈ 0.8–0.9, mean absolute errors ≈ 0.8–1.0). Hence, the Climate Hazards Center Infrared Temperature with Stations (CHIRTS max ) dataset should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications.
Accurately assessing the impacts of extreme climate events (ECEs) on crop yield can help develop effective agronomic practices to deal with climate change impacts. Process-based crop models are useful tools to evaluate climate change impacts on crop productivity but are usually limited in modelling the effects of ECEs due to over-simplification or vague description of certain process and uncertainties in parameterization. In this study, we firstly developed a hybrid model by incorporating the APSIM model outputs and growth stage-specific ECEs indicators (i.e. frost, drought and heat stress) into the Random Forest (RF) model, with the multiple linear regression (MLR) model as a benchmark. The results showed that the APSIM + RF hybrid model could explain 81% of the observed yield variations in the New South Wales wheat belt of south-eastern Australia, which had a 33% improvement in modelling accuracy compared to the APSIM model alone and 19% improvement compared to the APSIM + MLR hybrid model. Drought events during the grain-filling and vegetative stages and heat events immediately prior to anthesis were identified as the three most serious ECEs causing yield losses. We then compared the APSIM + RF hybrid model with the APSIM model to estimate the effects of future climate change on wheat yield. It was interesting to find that future yield projected from single APSIM model might have a 1–10% overestimation compared to the APSIM + RF hybrid model. The APSIM + RF hybrid model indicated that we were underestimating the effects of climate change and future yield might be lower than predicted using single APSIM informed modelling due to lack of adequately accounting for ECEs-induced yield losses. Increasing heat events around anthesis and grain-filling periods were identified to be major factors causing yield losses in the future. Therefore, we conclude that including the effects of ECEs on crop yield is necessary to accurately assess climate change impacts. We expect our proposed hybrid-modelling approach can be applied to other regions and crops and offer new insights of the effects of ECEs on crop yield.
The WOFOST cropping systems model has been applied operationally over the last 25 years as part of the MARS crop yield forecasting system. In this paper we provide an updated description of the model and reflect on the lessons learned over the last 25 years. The latter includes issues like system performance, model sensitivity, spatial model setup, parameterization and calibration approaches as well as software implementation and version management. Particularly for spatial model calibrations we provide experience and guidelines on how to execute calibrations and how to evaluate WOFOST model simulation results, particularly under conditions of limited field data availability. As an open source model WOFOST has been a success with at least 10 different implementations of the same concept. An overview is provided for those implementations which are managed by MARS or Wageningen groups. However, the proliferation of WOFOST implementations has also led to questions on the reproducibility of results from different implementations as is demonstrated with an example from MARS. In order to certify that the different WOFOST implementations and versions available can reproduce basic sets of inputs and outputs we make available a large set of test cases as appendix to this publication. Finally, new methodological extensions have been added to WOFOST in simulating the impact of nutrients limitations, extreme events and climate variability. Also, a difference is made in the operational and scientific versions of WOFOST with different licensing models and possible revenue generation. Capitalizing both on academic development as well as model testing in real-world situations will help to enable new applications of the WOFOST model in precision agriculture and smart farming.
Climate change implies higher frequency and magnitude of agroclimatic extremes threatening plant production and the provision of other ecosystem services. This review is motivated by a mismatch between advances made regarding deeper understanding of abiotic stress physiology and its incorporation into ecophysiological models in order to more accurately quantifying the impacts of extreme events at crop system or higher aggregation levels. Adverse agroclimatic extremes considered most detrimental to crop production include drought, heat, heavy rains/hail and storm, flooding and frost, and, in particular, combinations of them. Our core question is: How have and could empirical data be exploited to improve the capability of widely used crop simulation models in assessing crop impacts of key agroclimatic extremes for the globally most important grain crops? To date there is no comprehensive review synthesizing available knowledge for a broad range of extremes, grain crops and crop models as a basis for identifying research gaps and prospects. To address these issues, we selected eight major grain crops and performed three systematic reviews using SCOPUS for period 1995-2016. Furthermore, we amended/complemented the reviews manually and performed an in-depth analysis using a sub-sample of papers. Results show that by far the majority of empirical studies (1631 out of 1772) concentrate on the three agroclimatic extremes drought, heat and heavy rain and on the three major staples wheat, maize and rice (1259 out of 1772); the concentration on just a few has increased over time. With respect to modelling studies two model families, i.e. CERES-DSSAT and APSIM, are clearly dominating for wheat and maize; for rice, ORYZA2000 and CERES-Rice predominate and are equally strong. For crops other than maize and wheat the number of studies is small. Empirical and modelling papers don't differ much in the proportions the various extreme events are dealt with-drought and heat stress together account for approx. 80% of the studies. There has been a dramatic increase in the number of papers, especially after 2010. As a way forward, we suggest to have very targeted and well-designed experiments on the specific crop impacts of a given extreme as well as of combinations of them. This in particular refers to extremes addressed with insufficient specificity (e.g. drought) or being under-researched in relation to their economic importance (heavy rains/storm and flooding). Furthermore, we strongly recommend extending research to crops other than wheat, maize and rice.
Impacts of climate change on European agricultural production, land use and the environment depend on its
impact on crop yields. However, many impact studies assume that crop management remains unchanged in
future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties' thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between −6 and +21% considering NoAd management, whereas impacts with Opt management varied between +12 and +53%, and those under Act management between −2 and +27%. However, relative yield increases under climate change increased to +17 and +51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers.
To reduce the dependence on local expert knowledge, which is important for large-scale crop modelling
studies, we analyzed sowing dates and rules for maize (Zea mays L.) and sorghum (Sorghum bicolor (L.))
at three locations in Burkina Faso with strongly decreasing rainfall amounts from south to north. We
tested in total 22 methods to derive optimal sowing dates that result in highest water-limited yields and
lowest yield variation in a reproducible and objective way. The WOFOST crop growth simulation model
was used.
We found that sowing dates that are based on local expert knowledge, may work quite well for Burkina
Faso and for West Africa in general. However, when no a priori information is available, maize should be
sown between Julian days 160 and 200, with application of the following criteria: (a) cumulative rainfall
in the sowing window is ≥3 cm or available soil moisture content is >2 cm in the moderately dry central
part of Burkina Faso, (b) cumulative rainfall in this period is ≥2 cm or available soil moisture content is >1 cm in the more humid regions in the southern part of Burkina Faso. Sorghum should also be sown between Julian days 160 and 200 with application of the following criteria: (a) in the dry northern part of Burkina Faso he long duration sorghum variety should be sown when cumulative rainfall is ≥2 cm in the sowing window, nd the short duration sorghum variety should be sown later when cumulative rainfall is ≥3 cm, (b) in central urkina Faso sowing should start when cumulative rainfall in this period is ≥2 cm or when available soil oisture content is >1 cm. Sowing date rules are shown to be generally crop and location specific and are not eneric for West Africa. However, the required precision of the sowing rules appears to rapidly decrease with increasing duration and intensity of the rainy season. Sowing delay as a result of, for example, labour nstraints, has a disastrous effect on rainfed maize and sorghum yields, particularly in the northern part of West Africa with low rainfall. Optimization of sowing dates can also be done by simulating crop yields in a time window of two months around a predefined sowing date. Using these optimized dates appears to esult in a good estimate of the maximal mean rainfed yield level.
Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
Dependence on uncertain rainfall and exposure to unmitigated climate risk are major obstacles in efforts to sustainably intensify agricultural production and enhance rural livelihoods. There is generally enough seasonal total rainfall; the challenge is its poor distribution over time and across the season. The amount of water available to plants strongly depends on the rainy season's onset, length, temporal distribution and cessation and can indirectly indicate the climatic suitability of the crop and its chances of success or failure in a season. Thus, the objective was to determine rainfall pattern; temporal distribution, onset, cessation and length of growing seasons in the tropical sub-humid and a semi-arid regions with contrasting rainfall patterns and agricultural potential in central highlands of Kenya. The study was carried out in Maara and Meru South Sub-Counties in Tharaka Nithi County and Mbeere North and South Sub-Counties in Embu County of the central highlands of Kenya (CHK). Central highlands of Kenya cover both areas with high potential for crop production and low potential, attributed to rainfall differences. Meteorological data were sourced from Kenya Metrological Department (KMD) headquarters and research stations within the study areas. Length of growing season, onset and cessation dates for both Long (LR) and short (SR) rains seasons were determined based on historical rainfall data using RAIN software and derived using various spatial analysis tools in ArcGIS software and presented spatially. Generally there was high frequency of dry spells of at least 5 days length in all the sites with Kiamaogo site having the highest (84 occurrences during LR season) and Kiambere having the least (44 occurrences during LR season) in 10 years. The occurrence of dry spells longer than 15 days in a season was more rampant in the lower altitude parts (semi-arid regions) of the study area as reflected by the Kiambere, Kiritiri, Machang’a and Kamburu sites in both seasons. For the higher altitude regions, average LR onset, representative of the normal/conventional growing period, ranged from 22nd to 26th March to end of April in the region. For the lower altitude region, it ranged from 16th to 30th March. For SR, onset was generally earlier in the high altitude areas with Kiamaogo having the earliest on 13th October. In the low altitude region, onset was comparatively late compared to the higher potential region, but unlike the LR season, spatial and temporal variation was narrower. The high frequency of dry spells more than 15 days long, coupled with the generally low total amount of rainfall receive per season makes agriculture a risk venture. Homogeneity test revealed that the generated onset and cessation dates for the two rain seasons were homogeneous over the 10 years for each of the seven stations. This indicates that, there has been no shift in onset and cessation within the period under consideration. Dynamic derivation of the spatial onset and cessation data at a local scale can be useful in monitoring shifts in onset dates and hence advice small scale farmers and other stakeholders in agriculture sector accordingly in the quest for enhanced agricultural productivity.