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Zhang, S., Tao, F., & Zhang, Z. (2016). Changes in extreme temperatures and their impacts on rice yields in southern China from 1981 to 2009. Field Crops Research, 189, 43–50.
Abstract: Extreme temperature impacts on field crop are of key concern and increasingly assessed, however the studies have seldom taken into account the automatic adaptations such as shifts in planting dates, phenological dynamics and cultivars. In this present study, trial data on rice phenology, agro-meteorological hazards and yields during 1981-2009 at 120 national agro-meteorological experiment stations were used. The detailed data provide us a unique opportunity to quantify extreme temperature impacts on rice yield more precisely and in a setting with automatic adaptations. In this study, changes in an accumulated thermal index (growing degree day, GDD), a high temperature stress index (>35 degrees C high temperature degree day, HDD), and a cold stress index (<20 degrees C cold degree day, CDD), were firstly investigated. Then, their impacts on rice yield were further quantified by a multivariable analysis. The results showed that in the past three decades, for early rice, late rice and single rice in western part, and single rice in other parts of the middle and lower reaches of Yangtze River, respectively, rice yield increased by 5.83%, 1.71%, 8.73% and 3.49% due to increase in GDD. Rice yield was generally more sensitive to high temperature stress than to cold temperature stress. It decreased by 0.14%, 0.32%, 0.34% and 0.14% due to increase in HDD, by contrast increased by 1.61%, 0.26%, 0.16% and 0.01% due to decrease in CDD, respectively. In addition, decreases in solar radiation reduced rice yield by 0.96%, 0.13%, 9.34% and 6.02%. In the past three decades, the positive impacts of increase in GDD and the negative impacts of decrease in solar radiation played dominant roles in determining overall climate impacts on yield. However, with climate warming in future, the positive impacts of increase in GDD and decrease in CDD will be offset by increase in HDD, resulting in overall negative climate impacts on yield. Our findings highlight the risk of heat stress on rice yield and the importance of developing integrated adaptation strategies to cope with heat stress.
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Nendel, C., Wieland, R., Mirschel, W., Specka, X., Guddat, C., & Kersebaum, K. C. (2013). Simulating regional winter wheat yields using input data of different spatial resolution. Field Crops Research, 145, 67–77.
Abstract: The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km(2)). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 +/- 0.87 t ha(-1) for the federal state. Use of a 100 m x 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 +/- 1.47 t ha(-1)). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 +/- 1.37 t ha(-1)); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 +/- 1.17 t ha(-1)). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992-2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model. (C) 2013 Elsevier B.V. All rights reserved.
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Mittenzwei, K., Persson, T., Höglind, M., & Kværnø, S. (2017). Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agric. Syst., 153, 118–126.
Abstract: Highlights • A framework to study climate and policy uncertainty in agriculture is presented. • Combining both sources of uncertainty has ambiguous effects on agriculture. • Uncertainty needs to be highlighted in modelling tools for policy analysis. Abstract Farmers are exposed to climate change and uncertainty about how that change will develop. As farm incomes, in Norway and elsewhere, greatly depend on government subsidies, the risk of a policy change constitutes an additional uncertainty source. Hence, climate and policy uncertainty could substantially impact agricultural production and farm income. However, these sources of uncertainty have, so far, rarely been combined in food production analyses. The aim of this study was to determine the effects of a combination of policy and climate uncertainty on agricultural production, land use, and social welfare in Norway. Output yield distributions of spring wheat and timothy, a major forage grass, from simulations with the weather-driven crop models, CSM-CERES-Wheat and, LINGRA, were processed in the a stochastic version Jordmod, a price-endogenous spatial economic sector model of the Norwegian agriculture. To account for potential effects of climate uncertainty within a given future greenhouse gas emission scenario on farm profitability, effects on conditions that represented the projected climate for 2050 under the emission scenario A1B from the 4th assessment report of the Intergovernmental Panel on Climate Change and four Global Climate Models (GCM) was investigated. The uncertainty about the level of payment rates at the time farmers make their management decisions was handled by varying the distribution of payment rates applied in the Jordmod model. These changes were based on the change in the overall level of agricultural support in the past. Three uncertainty scenarios were developed and tested: one with climate change uncertainty, another with payment rate uncertainty, and a third where both types of uncertainty were combined. The three scenarios were compared with results from a deterministic scenario where crop yields and payment rates were constant. Climate change resulted in on average 9% lower cereal production, unchanged grass production and more volatile crop yield as well as 4% higher farm incomes on average compared to the deterministic scenario. The scenario with a combination of climate change and policy uncertainty increased the mean farm income more than a scenario with only one source of uncertainty. On the other hand, land use and farm labour were negatively affected under these conditions compared to the deterministic case. Highlighting the potential influence of climate change and policy uncertainty on the performance of the farm sector our results underline the potential error in neglecting either of these two uncertainties in studies of agricultural production, land use and welfare.
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Challinor, A. J., Müller, C., Asseng, S., Deva, C., Nicklin, K. J., Wallach, D., et al. (2017). Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst., 159, 296–306.
Abstract: Highlights
• 14 criteria for use of crop models in assessments of impacts, adaptation and risk • Working with stakeholders to identify timing of risks is key to risk assessments. • Multiple methods needed to critically assess the use of climate model output • Increasing transparency and inter-comparability needed in risk assessments
Abstract
Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.
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Schönhart, M., Schauppenlehner, T., Kuttner, M., Kirchner, M., & Schmid, E. (2016). Climate change impacts on farm production, landscape appearance, and the environment: Policy scenario results from an integrated field-farm-landscape model in Austria. Agricultural Systems, 145, 39–50.
Abstract: Climate change is among the major drivers of agricultural land use change and demands autonomous farm adaptation as well as public mitigation and adaptation policies. In this article, we present an integrated land use model (ILM) mainly combining a bio-physical model and a bio-economic farm model at field, farm and landscape levels. The ILM is applied to a cropland dominated landscape in Austria to analyze impacts of climate change and mitigation and adaptation policy scenarios on farm production as well as on the abiotic environment and biotic environment. Changes in aggregated total farm gross margins from three climate change scenarios for 2040 range between + 1% and + 5% without policy intervention” and compared to a reference situation under the current climate. Changes in aggregated gross margins are even higher if adaptation policies are in place. However, increasing productivity from climate change leads to deteriorating environmental conditions such as declining plant species richness and landscape appearance. It has to be balanced by mitigation and adaptation policies taking into account effects from the considerable spatial heterogeneity such as revealed by the ILM. (C) 2016 Elsevier Ltd. All rights reserved.
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