Process-based Crop Models Research Articles

Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha−1, and 0.85 t ha−1, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

Evidence of crop production losses in West Africa due to historical global warming in two crop models

Achieving food security goals in West Africa will depend on the capacity of the agricultural sector to feed the rapidly growing population and to moderate the adverse impacts of climate change. Indeed, a number of studies anticipate a reduction of the crop yield of the main staple food crops in the region in the coming decades due to global warming. Here, we found that crop production might have already been affected by climate change, with significant yield losses estimated in the historical past. We used a large ensemble of historical climate simulations derived from an atmospheric general circulation model and two process-based crop models, SARRA-H and CYGMA, to evaluate the effects of historical climate change on crop production in West Africa. We generated two ensembles of 100 historical simulations of yields of sorghum and millet corresponding to two climate conditions for each crop model. One ensemble is based on a realistic simulation of the actual climate, while the other is based on a climate simulation that does not account for human influences on climate systems (that is, the non-warming counterfactual climate condition). We found that the last simulated decade, 2000–2009, is approximately 1 °C warmer in West Africa in the ensemble accounting for human influences on climate, with more frequent heat and rainfall extremes. These altered climate conditions have led to regional average yield reductions of 10–20% for millet and 5–15% for sorghum in the two crop models. We found that the average annual production losses across West Africa in 2000–2009 associated with historical climate change, relative to a non-warming counterfactual condition (that is, pre-industrial climate), accounted for 2.33–4.02 billion USD for millet and 0.73–2.17 billion USD for sorghum. The estimates of production losses presented here can be a basis for the loss and damage associated with climate change to date and useful in estimating the costs of the adaptation of crop production systems in the region.

Maize yield under a changing climate: The hidden role of vapor pressure deficit

Temperatures over the next century are expected to rise to levels detrimental to crop growth and yield. As the atmosphere warms without additional water vapor input, vapor pressure deficit (VPD) increases as well. Increased temperatures and accompanied elevated VPD levels can both lead to negative impacts on crop yield. The independent importance of VPD, however, is often neglected or conflated with that from temperature due to a tight correlation between the two climate factors. We used a coupled process-based crop (MAIZSIM) and soil (2DSOIL) model to gain a mechanistic understanding of the independent roles temperature and VPD play in crop yield projections, as well as their interactions with rising CO2 levels and changing precipitation patterns. We found that by separating out the VPD effect from rising temperatures, VPD increases had a greater negative impact on yield (12.9 ± 1.8%, increase in VPD associated with 2 °C warming) compared to that from warming (8.5 ± 1.4%, the direct effect of 2 °C warming). The negative impact of these two factors varied with precipitation levels and influenced yield through separate mechanisms. Warmer temperatures caused yield loss mainly through shortening the growing season, while elevated VPD increased water loss and triggered several water stress responses such as reduced photosynthetic rates, lowered leaf area development, and shortened growing season length. Elevated CO2 concentrations partially alleviated yield loss under warming or increased VPD conditions through water savings, but the impact level varied with precipitation levels and was most pronounced under drier conditions. These results demonstrate the key role VPD plays in crop growth and yield, displaying a magnitude of impact comparative to temperature and CO2. A mechanistic understanding of the function of VPD and its relation with other climate factors and management practices is critical to improving crop yield projections under a changing climate.

Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model

Process-based crop models are popular scientific tools to study the impacts of environmental conditions and management decisions on crop growth. Some cultivar parameters in crop models cannot be measured directly and need to be estimated. In this research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). Both theoretical and real-world evaluations were conducted to compare the performances of these two methods. The maize yields from 2003 to 2006 were used for model calibration, and the yields from 2007 to 2013 were used for model validation. Both GLUE and DREAM performed well in the theoretical and real-world evaluation. During the validation period (2007–2013), when the heteroscedastic model error assumption was adopted in DREAM, on average approximate 90% of observed yield values were captured in the 95% confidence band of DREAM (P-factor = 90.47%), which was larger than that using GLUE (P-factor = 80.93%). Meanwhile the uncertainty bands of DREAM (R-factor = 4.42) were wider than those of GLUE (R-factor = 2.32). If only one parameter set was allowed to be used in the simulation, the weighted mean parameter values according to the likelihood of each parameter set performed better than the parameter set with maximum likelihoods for GLUE while the opposite is true for DREAM. But considering future analysis in the real-world evaluation, the moderate performance of these two methods suggests that a single parameter set obtained by neither GLUE nor DREAM is satisfactory and ensemble simulation is needed. Overall, GLUE and DREAM had similar performance, but GLUE is more convenient and simpler to use than DREAM. So we think GLUE is a better choice than DREAM for estimating cultivar parameters of APSIM-maize.

Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia

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.

Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity

Elevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

Sensitivity Analysis of Plant- and Cultivar-Specific Parameters of APSIM-Sugar Model: Variation between Climates and Management Conditions

With increasing demand for food and energy, there is a great need for improving sugarcane productivity. New cultivars and management strategies can be assessed using process-based crop models. Information on cultivars needs to be updated frequently, but it is still limited in most crop models. Therefore, it is important to identify possible candidates for varietal parameterization and calibration. Because sensitivity analysis is computationally expensive, we used a less expensive emulator-based approach to conduct a global sensitivity analysis using the apsimr package and GEM-SA software. We studied the sensitivity of four yield outputs of the APSIM-Sugar model to 13 parameters in rainfed and irrigated conditions in Japan and Sri Lanka. Unlike previous studies, our aim was to give comprehensive insights into the variation in sensitivity due to variation in climate. The results confirmed distinct variation of parameter influence between climates and between management conditions. We identify possible candidates for parameterization and calibration of new cultivars for APSIM-Sugar under different environments, and show the effect of variation in climate on variation in parameter influence under different management conditions. It was confirmed that both radiation use efficiency and transpiration efficiency were sensitive and have to be examined to use new cultivars, though these are not listed as cultivar parameters.

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States.

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to −34% (−17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to −37% loss by extreme drought (−32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.

Improving process-based crop models to better capture genotype×environment×management interactions.

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments

Integrated models are crucial for evaluation of the complex interactions and trade-offs among policy choices and socioeconomic, technical, and environmental processes. The use of process-based crop models as components of integrated models offers the possibility of significantly improving such analyses; however, challenges exist in terms of simulation scales and degree of integration. Therefore, this study evaluates the applicability of coarser-than-daily simulation time steps to simulate long-term crop yields in integrated models, and the impacts of aggregated weather input data on yields for a water-driven crop-process model based on the FAO AquaCrop model. We ran simulations at daily, semi-weekly, and weekly time steps in conjunction with coarser temporal resolution (weekly) weather input data for three crops in four locations over ten years to represent a range of crops and growing environments. Simulation results were compared to a reference case from AquaCrop using daily time step with daily weather data. Model skill for simulating crop biomass and yield and water demands was assessed statistically for each of these four hypothetical farms. Visual representations were also used to compare simulated soil moisture, crop canopy, and actual evapotranspiration values. Weekly climate data led to overestimation of crop biomass and yield regardless of the time step used. High agreements and low bias errors were realized for crop production and water estimates at daily and semi-weekly time steps, whereas weekly simulations showed poorer performance. Longer time steps intensified the impacts of weather input data aggregation, and overestimation became more pronounced with increases in time step length. The findings have important implications for integrated assessments that couple crop models with other socioeconomic, environmental, or hydrologic models, and provide guidance for modelers involved in interdisciplinary agricultural and water resources applications, including policy assessments, evaluation of water and food security, and resource use and efficiency under climate change.

Estimating cropland carbon fluxes: A process-based model evaluation at a Swiss crop-rotation site

In support of climate change mitigation strategies and food security, there is an ever-increasing requirement to understand the key drivers of cropland yields and carbon (C) budgets. Process-based crop models, driven by meteorological observations, can provide realistic simulations and diagnosis of the variability in crop C budgets. However, the field-scale observations required to validate the models, particularly across multiple growing seasons and crop types, are seldom available.This research evaluates the Soil-Plant-Atmosphere Crop (SPA-Crop) model for estimating the C fluxes of multiple crop types and seasons at the Swiss long-term flux site Oensingen (CH-OE2). SPA-Crop is a detailed parametric model, simulating ecosystem photosynthesis and water balance at fine temporal (half-hourly time-steps) and vertical scales (multiple canopy and soil layers). We initialised the model at the point-scale using management information and meteorological observations recorded during ten crop seasons from 2004 to 2015. SPA-Crop has been developed for simulating winter cereals (wheat and barley), we further evaluated the model performance for these crops at CH-OE2 and extended the simulation to include a crop-specific calibration for winter rapeseed and pea crops. Model estimates of net ecosystem exchange (NEE) of carbon dioxide (CO2), ecosystem respiration (Reco), and gross primary production (GPP) were evaluated based on gap-filled and partitioned eddy covariance (EC) fluxes. Further comparisons were made between modelled and observed yields.The daily SPA-Crop estimates had a high agreement with the EC measurements across the different crop types with a mean R2 of the NEE of 0.74 (wheat), 0.73 (barley), 0.71 (rapeseed) and 0.69 (pea). The modelled yield estimates had biases ranging from −22% (barley, measured-model difference −70 gC m–2; 0.31 t ha−1) to +56% (rapeseed, measured-model difference −85 gC m–2; −0.38 t ha−1), with the largest bias of wheat being only −13% (measured-model difference 34 gC m–2; 0.15 t ha−1) across four wheat seasons. We recommend future SPA-Crop developments – particularly with regards to improving the precision of Reco estimates. Our research demonstrates that SPA-Crop is a reliable tool for simulating the C budget and yield across the four crop types: winter wheat, winter barley, winter rapeseed, and peas.

Simulating soil fertility management effects on crop yield and soil nitrogen dynamics in field trials under organic farming in Europe

Soil fertility building measures should be explored at the short and long-term for an adequate evaluation of their impact on sustaining yields and of its environmental consequences in crop rotations under organic farming. For such a purpose, process-based crop models are potential useful tools to complement and upscale field observations under a range of soil and climatic conditions. Organic rotations differ in soil fertility dynamics in comparison to conventional farming but very few modelling studies have explicitly considered this specific situation. Here, we evaluate the FASSET model to predict the effects of different fertility management options in organic crop rotations on dry matter (DM) and nitrogen (N) yield, and soil N dynamics, including N2O emissions. For that, we used data from seven short and long-term field experiments in different agro-climatic environments in Europe (Norway, Denmark, Poland, Switzerland, Italy and Spain) including climate, soil and management data. Soil fertility building measures covered fertilization type, green manures, cover crops, tillage, crop rotation composition and management (organic or conventional). Model performance was evaluated by comparing observed and simulated values of crop DM and N yield, soil mineral N and nitrous oxide (N2O) emissions using five complementary statistical indices. The model closely reproduced most observed DM and N yield trends and effects of soil fertility building measures in arable crops, particularly in cereals. Contrary, yields of grass-clover, especially N, were generally reproduced with low degree of accuracy. Model performance for simulating soil mineral N depended on site and the availability of soil and management information. Although high uncertainty was associated to the simulation of soil N dynamics, differences of cumulative N2O emissions between fertility building measures were reflected in model outputs. Aspects for modelling improvement include cover crop growth and decomposition, biological N fixation (BNF) or weed and pest soil-crop interactions. It is concluded that FASSET can be successfully used to investigate the impact of fertilization type, green manures, tillage and management (organic or conventional) on crop productivity and to a certain extent on soil N dynamics including soil N2O emissions at different soils and climates in organic farming in Europe.

Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries

ABSTRACTA rice yield estimation system was developed based on the crop growth model ORYZA and SAR-derived key information such as start of season (SOS) and leaf area growth rate. Results from study sites in South and South-east Asian countries suggest that incorporating remote sensing data, specifically Synthetic aperture radar (SAR), into a process-based crop model improves the spatial distribution of yield estimates. This article highlights the detailed methodology of SAR data incorporation into crop yield simulation and comprehensive validation of yield forecast and estimates in the Philippines, Vietnam, Cambodia, Thailand, and Tamil Nadu, India. Remote sensing data assimilation into a crop model effectively captures the responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. A process-based crop simulation model is used in the system to ensure that climate information is captured, and this provides the capacity to deliver a mid-season yield forecast for national planning and policy for rice. Good agreement between SAR-based yield and crop-cut-based yield and official yield statistics and ensuring efficiency of the processing suggest that the system is a promising solution for the needed timely information on rice yield for application in food security and policies, climate disaster management, and crop insurance programs.

Global historical soybean and wheat yield loss estimates from ozone pollution considering water and temperature as modifying effects

Ozone pollution can severely diminish crop yields. Its damaging effects depend, apart from ozone concentration, on crop, cultivar, water status, temperature and CO2 concentration. Previous studies estimating global yield loss from ozone pollution did not consider all of these co-factors and climate change impact studies on crop yields typically ignore ozone pollution. Here we introduce an ozone damage module for the widely used process-based crop model LPJmL. The implementation describes ozone uptake through stomata, internal detoxification and short- and long-term effects on productivity and phenology, dynamically accounting for all listed co-factors. Using this enhanced model we estimate historical global yield losses from ozone pollution for wheat and soybeans. We divide wheat into “Western” and “Asian” to account for higher ozone sensitivities in Asian types. We apply daily ozone concentrations obtained from six chemistry-transport models provided by the ACCMIP and HTAP2 projects.Our implementation of ozone damage follows expected dynamics, for example damage amplification under irrigation. The model is able to reproduce results from chamber and field studies. Historical ozone-induced losses between 2008 and 2010 vary between countries, and we estimate these between 2 and 10% of ozone-free yields for soybeans, between 0 and 27% for Western wheat and 4 and 39% for Asian wheat.Our study highlights the threat of ozone pollution for global crop production and improves over previous studies by considering co-factors of ozone damage. Uncertainties of our study include the extrapolation from rather few point observations to the globe, possible biases in ozone data, omission of sub-daily fluctuations in ozone concentration or stomatal conductance and the averaging of different cultivars across regions. We suggest performing further field-scale experimental studies of ozone effects on crops, as these are currently rare but would be particularly helpful to evaluate models and to estimate large-scale effects of ozone.

Decreases in global beer supply due to extreme drought and heat

Beer is the most popular alcoholic beverage in the world by volume consumed, and yields of its main ingredient, barley, decline sharply in periods of extreme drought and heat. Although the frequency and severity of drought and heat extremes increase substantially in range of future climate scenarios by five Earth System Models, the vulnerability of beer supply to such extremes has never been assessed. We couple a process-based crop model (decision support system for agrotechnology transfer) and a global economic model (Global Trade Analysis Project model) to evaluate the effects of concurrent drought and heat extremes projected under a range of future climate scenarios. We find that these extreme events may cause substantial decreases in barley yields worldwide. Average yield losses range from 3% to 17% depending on the severity of the conditions. Decreases in the global supply of barley lead to proportionally larger decreases in barley used to make beer and ultimately result in dramatic regional decreases in beer consumption (for example, -32% in Argentina) and increases in beer prices (for example, +193% in Ireland). Although not the most concerning impact of future climate change, climate-related weather extremes may threaten the availability and economic accessibility of beer.

The impact of future climate change and potential adaptation methods on Maize yields in West Africa

Maize (Zea mays) is one of the staple crops of West Africa and is therefore of high importance with regard to future food security. The ability of West Africa to produce enough food is critical as the population is expected to increase well into the twenty-first century. In this study, a process-based crop model is used to project maize yields in Africa for global temperatures 2 K and 4 K above the preindustrial control. This study investigates how yields and crop failure rates are influenced by climate change and the efficacy of adaptation methods to mitigate the effects of climate change. To account for the uncertainties in future climate projections, multiple model runs have been performed at specific warming levels of + 2 K and + 4 K to give a better estimate of future crop yields. Under a warming of + 2 K, the maize yield is projected to reduce by 5.9% with an increase in both mild and severe crop failure rates. Mild and severe crop failures are yields 1 and 1.5 standard deviations below the observed yield. At a warming of + 4 K, the results show a yield reduction of 37% and severe crop failures which previously only occurred once in 19.7 years are expected to happen every 2.5 years. Crops simulated with a resistance to high temperature stress show an increase in yields in all climate conditions compared to unadapted crops; however, they still experience more crop failures than the unadapted crop in the control climate.

Simulated impact of paleoclimate change on Fremont Native American maize farming in Utah, 850–1449 CE, using crop and climate models

The Fremont were members of an expansive maize-based Ancestral Puebloan (AP) cultural complex who disappeared from Utah between the 12th and 13th centuries CE. This period brackets that of a climatic transition in the Southwest from the warm, dry Medieval Climate Anomaly (MCA, ca. 850–1350 CE) to the cool, hydro-climatically variable Little Ice Age (LIA, ca. 1350–1850 CE). We simulated maize (Zea mays) crop productivity for Fremont AP archaeological sites in Utah between 850 and 1449 CE using a process-based crop model driven by climatologies from a statistically downscaled a climate model. We compared the model-simulated crop yields to time-series of archaeological site occupations given by spatially discrete, chronologically summed probability distributions (SPDs) of radiocarbon-dated Fremont artifacts. We found that the anomalous abandonment of different sites throughout Utah may be explained by site-specific combinations of reduced mean yield due to volatile year-to-year yields caused by increasing temperature variability, increasing hydro-climatic variability, and loss of soil quality, which depended on crop management strategy. In other words, we model the elimination of the Fremont AP ecological niche by exogenous influences of temperature and precipitation variability at the MCA-LIA transition and endogenous degradation of soil from organic carbon and nitrogen loss. Our method has broad applicability to contexts of low-technology, dryland farming human-environmental interactions.

Vineyard mulching as a climate change adaptation measure: Future simulations for Alentejo, Portugal

Climate change projections for the next decades are expected to bring important challenges to the Portuguese viticulture. More specifically, for the wine region of Alentejo, in Southern Portugal, the projected warming and drying are expected to have detrimental impacts on grapevine physiology and ultimately on yields. The present study assesses the adaptation potential of mulching for maintaining current grapevine yield levels in the region. For this purpose, the STICS process-based crop model was used to simulate future (2021–2080) grapevine yields in the 8 sub-regions of Alentejo (with Denomination of Origin). Several datasets for weather variables, soil characteristics, topographic features and management practices were defined independently for each sub-region. Simulations comprise both non-mulching and mulching experiments over the next 60 years, under the climate change scenario RCP8.5. Although both non-mulching and mulching simulations suggest a gradual yield decrease in the future, mulching mitigates these decreases by 10 to 25%. Furthermore, the results show that mulching can reduce the yield decreasing trend, from −0.75%/year to −0.66%/year. In effect, mulching is expected to provide yield gains over the full simulated time period, being the benefits particularly apparent towards the end of the target period (2061–2080; warmest years of simulation). Mulching is a cost-effective adaptation measure that may be easily adopted by growers on the short-term. Nonetheless, this strategy alone might not be enough to fully mitigate yield losses and additional / complementary measures should be envisioned to warrant the sustainability of the Alentejo winemaking sector under futures climates.

Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM)

Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.

Can we use crop modelling for identifying climate change adaptation options?

Climate model projections coupled with process-based crop models are advocated for assessing impacts of climate change on crop yields and for informing crop-level adaptations. However, most reported studies are vague on the choice of the global circulation models (GCMs) for climate projections, and on the corresponding uncertainty with this type of model simulations. Here we investigated whether climate-crop modelling can be used for identifying crop management-level adaptation options. We focused our analyses on a case study for maize in southern Africa using the APSIM crop growth model and projections from 17 individual climate models for the period 2017–2060 for the contrasting representative concentration pathways 2.6 and 8.5. Intensification of nitrogen fertiliser use (from 30 to 90 kg N ha−1) was simulated as an example of a crop management-level adaptation to climate change. Uncertainties in crop yield predictions were about 30 to 60%, i.e. larger than expected crop responses to most management-level interventions or adaptations. Variation in simulated yields was caused by inter-seasonal rainfall variability and uncertainty with climate models. Some GCMs resulted in significantly different maize yield predictions, without any clear pattern across sites. Given these high uncertainties, we argue that crop modellers should be cautious when informing future crop management adaptation strategies based on climate-crop model ensembles. A better use of crop models is the simulation of crop responses to current weather variability aiming at the identification of crop management practices for coping with climate variability. Promising practices can then be evaluated with farmers on their feasibility over a range of plausible future biophysical and socio-economic farming conditions.