Testing Effects of Climate Change in Crop Models

Space and time related data generated is becoming ever more voluminous, noisy and heterogeneous outpacing the research efforts in the domain of climate. Nevertheless, this data portrays recent climate/ weather change patterns. Thus, insightful approaches are required to overcome the challenges when handling the so called “big data” to unravel the recent unprecedented climate change in particular, its variability, frequency and effects on key crops. Contemporary climate-crop models developed at least two decades ago are found to be unsuitable for analysing complex climate/weather data retrospectively. In this context, the chapter looks at the use of scalable time series analysis, namely ARIMA (Autoregressive integrated moving average) models and data mining techniques to extract new knowledge on the climate change effects on Malaysia's oil palm yield at the regional and administrative divisional scales. The results reveal recent trends and patterns in climate change and its effects on oil palm yield impossible otherwise e.g. Traditional statistical methods alone.

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Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial

10.5194/esd-2017-66 ◽

2017 ◽

Author(s):

Ben Parkes ◽

Dimitri Defrance ◽

Benjamin Sultan ◽

Philippe Ciais ◽

Xuhui Wang

Keyword(s):

Climate Change ◽

West Africa ◽

Rainwater Harvesting ◽

High Temperature Stress ◽

Climate Projections ◽

Maize Crop ◽

Crop Models ◽

Time Frequency ◽

The Mean ◽

Near Term

Abstract. The ability of a country or region to feed itself in the upcoming decades is a question of importance. The population in West Africa is expected to increase significantly in the next 30 years. The responses of food crops to short term climate change is therefore critical to the population at large and the decision makers tasked with providing food for their people. An ensemble of near term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic and near term future. The mean yields are not expected to alter significantly, while there is an increase in inter annual variability. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond one standard deviation during a period of 20 years. The increasing variability increases the frequency and intensity of crop failures across West Africa. The mean return frequency between mild maize crop failures from process based crop models increases from once every 6.8 years to once every 4.5 years. The mean return time frequency for severe crop failures (beyond 1.5 standard deviations) also almost doubles from once every 16.5 years to once every 8.5 years. Two adaptation responses to climate change, the adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model in an idealised sensitivity test. The generalised adoption of a cultivar resistant to high temperature stress during flowering is shown to be more beneficial than using rainwater harvesting by both increasing yields and the return frequency of crop failures.

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What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa?

Agricultural Systems ◽

10.1016/j.agsy.2013.12.006 ◽

2014 ◽

Vol 127 ◽

pp. 161-177 ◽

Cited By ~ 62

Author(s):

Heidi Webber ◽

Thomas Gaiser ◽

Frank Ewert

Keyword(s):

Climate Change ◽

Food Security ◽

Climate Change Adaptation ◽

Sub Saharan Africa ◽

Crop Models ◽

Sub Saharan

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Climate change impact evaluation in various regions in Europe on the base of ensemble modelling

10.5194/egusphere-egu2020-18052 ◽

2020 ◽

Author(s):

Jaromir Krzyszczak ◽

Piotr Baranowski ◽

Monika Zubik

Keyword(s):

Climate Change ◽

Response Surfaces ◽

Mitigation Strategies ◽

Climate Conditions ◽

Crop Models ◽

Climate Change Uncertainty ◽

Arable Fields ◽

Four Levels ◽

The Impact ◽

Adaptation Options

Climate change uncertainty largely complicates adaptation and risk management evaluation at the regional level, therefore new approaches for managing this uncertainty are still being developed. In this study three crop models (DNDC, WOFOST and DSSAT) were used to explore the utility of impact response surfaces (IRS) and adaptation response surfaces (ARS) methodologies (Pirttioja et al., 2015; Ruiz-Ramos et al., 2018).To build IRS, the sensitivity of modelled yield to systematic increments of changes in temperature (-1 to +6&#176;C) and precipitation (-30 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather. Four levels of CO2 (360, 447, 522 and 601 ppm) representing future conditions until 2070 were considered. In turn, to build ARS, adaptation options were: shortening or extending the crop cycle of the standard cultivar, sowing earlier or later than the standard date and additional irrigation. Preliminary data indicate that yields are declining with higher temperatures and decreased precipitation. Yield is more sensitive to changes in baseline temperature values and much less sensitive to changes in baseline precipitation values for arable fields in Finland, while for arable fields in Germany, ARS indicates yield sensitivity at a similar level for both variables. Also, our data suggests that some adaptation options provides increase of the yield up to 1500 kg/ha, which suggest that ARSs may be valuable tool for planning an effective adaptation treatments. This research shows how to analyze and assess the impact of adaptation strategies in the context of the high level of regional uncertainty in relation to future climate conditions. Developed methodology can be applied to other climatic zones to help in planning adaptation and mitigation strategies.This study has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the project: MSINiN, contract number: BIOSTRATEG3/343547/8/NCBR/2017

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Modelling impacts of climate change and alternative management interventions on the multi-functionality of agricultural landscapes in southern Africa

10.5194/egusphere-egu2020-21649 ◽

2020 ◽

Author(s):

Reimund Roetter ◽

Simon Scheiter ◽

Munir Hoffmann ◽

Kwabena Ayisi ◽

Paolo Merante ◽

...

Keyword(s):

Climate Change ◽

Ecosystem Services ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Agricultural Landscapes ◽

Sub Saharan Africa ◽

Ecosystem Functions ◽

Drought Risk ◽

Crop Models ◽

Landscape Level

On the background of increasing welfare and continued population growth, there is an ever-increasing pressure on land and other natural resources in many parts of the world. The situation is, however, particularly severe in the drylands of Sub-Saharan Africa. Southern African landscapes, composed of arable lands, tree orchards and rangelands, provide a range of important ecosystem functions. These functions are increasingly threatened by land use changes through competing claims on land by agriculture, tourism, mining and other sectors, and by environmental change, namely climate change and soil degradation. Among others, climate models project that drought risk in the region will increase considerably. Based on comprehensive data sets originating from previous groundwork by several collaborative projects on the functioning of these ecosystems, a number of biophysical and bio-economic models have been developed and evaluated. In the framework of the South African Limpopo Landscapes network (SALLnet) we have now refined and tailored these models for combined use for the assessment of changes in multiple functions of the prevailing agroecosystems when affected by alternative climate and land management scenarios - from field to regional scale. We apply vegetation models (such as aDGVM), crop models (such as APSIM) and integrative farm level models (e.g. agent-based) for different farming systems in conjunction with geo-referenced databases. Model outputs are combined to assess the impact of management x environment interactions on various ecosystem functions. Of special interest in our study are the ecosystem services related to the provision of food, feed and fuel, soil and water conservation, as well as recycling and restoring carbon and nutrients in soil. To illustrate how the combination of various modelling components can work in assessing management intervention effects under different environmental conditions on landscape level ecosystem services, a case study was defined in Limpopo province, South Africa. We investigated effects of current management practices and an intensification scenario over a longer period of years on soil organic carbon change under rangeland and arable land, potential erosion, productive water use, biomass production, monthly feed gaps, and rangeland habitat quality. Tentative results showed that sustainable intensification closed the livestock feed gap, but further reduced soil organic carbon. More generally, coupling the output of vegetation and crop models regionally calibrated with sound ground/ experimental data appears promising to provide meaningful insights into the highly complex interconnections of different ecosystem services at a landscape level.

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Global contribution of climate variability and trends to maize yield change in observations and crop models during 1980-2010

10.5194/egusphere-egu2020-21042 ◽

2020 ◽

Author(s):

Xiaomeng Yin ◽

Guoyong Leng

Keyword(s):

Climate Change ◽

Climate Variability ◽

Statistical Model ◽

Yield Loss ◽

Climate Change Impacts ◽

Maize Yield ◽

Climate Impacts ◽

Future Climate Change ◽

Crop Models ◽

Process Based Models

Understanding historical crop yield response to climate change is critical for projecting future climate change impacts on yields. Previous assessments rely on statistical or process-based crop models, but each has its own strength and weakness. A comprehensive comparison of climate impacts on yield between the two approaches allows for evaluation of the uncertainties in future yield projections. Here we assess the impacts of historical climate change on global maize yield for the period 1980-2010 using both statistical and process-based models, with a focus on comparing the performances between the two approaches. To allow for reasonable comparability, we develop an emulator which shares the same structure with the statistical model to mimic the behaviors of process-based models. Results show that the simulated maize yields in most of the top 10 producing countries are overestimated, when compared against FAO observations. Overall, GEPIC, EPIC-IIASA and EPIC-Boku show better performance than other models in reproducing the observed yield variations at the global scale. Climate variability explains 42.00% of yield variations in observation-based statistical model, while large discrepancy is found in crop models. Regionally, climate variability is associated with 55.0% and 52.20% of yield variations in Argentina and USA, respectively. Further analysis based on process-based model emulator shows that climate change has led to a yield loss by 1.51%-3.80% during the period 1980-1990, consistent with the estimations using the observation-based statistical model. As for the period 1991-2000, however, the observed yield loss induced by climate change is only captured by GEPIC and pDSSAT. In contrast to the observed positive climate impact for the period 2001-2010, CLM-Crop, EPIC-IIASA, GEPIC, pAPSIM, pDSSAT and PEGASUS simulated negative climate effects. The results point to the discrepancy between process-based and statistical crop models in simulating climate change impacts on maize yield, which depends on not only the regions, but also the specific time period. We suggest that more targeted efforts are required for constraining the uncertainties of both statistical and process-based crop models for future yield predictions.&#160;

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Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Canadian Journal of Soil Science ◽

10.4141/cjss-2014-076 ◽

2015 ◽

Vol 95 (1) ◽

pp. 49-61 ◽

Cited By ~ 9

Author(s):

Ted Huffman ◽

Budong Qian ◽

Reinder De Jong ◽

Jiangui Liu ◽

Hong Wang ◽

...

Keyword(s):

Climate Change ◽

Spring Wheat ◽

Input Data ◽

Crop Yields ◽

Regional Scale ◽

Climate Change Impacts ◽

Crop Productivity ◽

Canadian Prairies ◽

Crop Models

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES–Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha−1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

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