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

2015 ◽  
Vol 95 (1) ◽  
pp. 49-61 ◽  
Author(s):  
Ted Huffman ◽  
Budong Qian ◽  
Reinder De Jong ◽  
Jiangui Liu ◽  
Hong Wang ◽  
...  

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.

2021 ◽  
Vol 166 (3-4) ◽  
Author(s):  
Angelo C. Gurgel ◽  
John Reilly ◽  
Elodie Blanc

AbstractMany approaches have been used to investigate climate change impacts on agriculture. However, several caveats remain in this field: (i) analyses focus only on a few major crops, (ii) large differences in yield impacts are observed between projections from site-based crops models and Global Gridded Crop Models (GGCMs), (iii) climate change impacts on livestock are rarely quantified, and (iv) several causal relations among biophysical, environmental, and socioeconomic aspects are usually not taken into account. We investigate how assumptions about these four aspects affect agricultural markets, food supply, consumer well-being, and land use at global level by deploying a large-scale socioeconomic model of the global economy with detailed representation of the agricultural sector. We find global welfare impacts several times larger when climate impacts all crops and all livestock compared to a scenario with impacts limited to major crops. At the regional level, food budget can decrease by 10 to 25% in developing countries, challenging food security. The role of land area expansion as a major source of adaptation is highlighted. Climate impacts on crop yields from site-based process crop models generate more challenging socioeconomic outcomes than those from GGCMs. We conclude that the agricultural research community should expand efforts to estimate climate impacts on many more crops and livestock. Also, careful comparison of the GGCMs and traditional site-based process crop models is needed to understand their major implications for agricultural and food markets.


2019 ◽  
Vol 3 (3) ◽  
pp. 61-79
Author(s):  
S.N. Singh

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of climate change and its affect on agricultural productivity in Ethiopia. The main purpose of the research is to analyze the impact of climate change on the productivity of agricultural crops. Systematization literary sources and approaches for solving the problem associate were analyzed that indicates there is a significant adverse effect of climate change on agricultural productivity as well as allied fields. The relevance of the decision of this scientific problem is that the community participation and state interventions are required at grass-roots level. Investigation of the topic of climate change and agriculture in Ethiopia in the paper is carried out broadly in the following logical sequence at an appropriate empirical standard level. Methodological tools of the research methods were descriptive statistics and the year of research was 2018-19. The object of research is the chosen for Ethiopia as a whole and case study was carried out in Mettu Woreda to verify the significance. The paper presents the results of an empirical analysis of quantitative data, which showed that there is an adverse effect of climate change on agricultural productivity in the region. The climate change affects agricultural productivity and production through shortening of maturity period and to decreasing crop yields, changing livestock feed availability, affecting animal health growth and reproduction depressing the quality and quantity of the crops, changing distribution rate, contracting pastoral zones, expansion of tropical dry forests and expansion of desertification etc.The research empirically confirms and theoretically proves that highlights the coordination between state and local communities are required to combat the adverse effect of climate change. The results of the research can be useful for policy maker, researchers, academicians and other international organizations like UNEP and UNDP etc. Keywords: climate change, random sampling, descriptive statistics, crop productivity, food security and livestock.


2019 ◽  
Author(s):  
International Food Policy Research Institute (IFPRI)

2021 ◽  
Author(s):  
Onil Banerjee ◽  
Martin Cicowiez ◽  
Ana Rios ◽  
Cicero De Lima

In this paper, we assess the economy-wide impact of Climate Change (CC) on agriculture and food security in 20 Latin American and the Caribbean (LAC) countries. Specifically, we focus on the following three channels through which CC may affect agricultural and non-agricultural production: (i) agricultural yields; (ii) labor productivity in agriculture, and; (iii) economy-wide labor productivity. We implement the analysis using the Integrated Economic-Environmental Model (IEEM) and databases for 20 LAC available through the OPEN IEEM Platform. Our analysis identifies those countries most affected according to key indicators including Gross Domestic Product (GDP), international commerce, sectoral output, poverty, and emissions. Most countries experience negative impacts on GDP, with the exception of the major soybean producing countries, namely, Brazil, Argentina and Uruguay. We find that CC-induced crop productivity and labor productivity changes affect countries differently. The combined impact, however, indicates that Belize, Nicaragua, Guatemala and Paraguay would fare the worst. Early identification of these hardest hit countries can enable policy makers pre-empting these effects and beginning the design of adaptation strategies early on. In terms of greenhouse gas emissions, only Argentina, Chile and Uruguay would experience small increases in emissions.


Author(s):  
Ivo Machar ◽  
Marián Halás ◽  
Zdeněk Opršal

Regional climate changes impacts induce vegetation zones shift to higher altitudes in temperate landscape. This paper deals with applying of regional biogeography model of climate conditions for vegetation zones in Czechia to doctoral programme Regional Geography in Palacky University Olomouc. The model is based on general knowledge of landscape vegetation zonation. Climate data for model come from predicted validated climate database under RCP8.5 scenario since 2100. Ecological data are included in the Biogeography Register database (geobiocoenological data related to landscape for cadastral areas of the Czech Republic). Mathematical principles of modelling are based on set of software solutions with GIS. Students use the model in the frame of the course “Special Approaches to Landscape Research” not only for regional scenarios climate change impacts in landscape scale, but also for assessment of climate conditions for growing capability of agricultural crops or forest trees under climate change on regional level.


2021 ◽  
Vol 310 ◽  
pp. 108649
Author(s):  
Yong Chen ◽  
Gary W. Marek ◽  
Thomas H. Marek ◽  
Dana O. Porter ◽  
David K. Brauer ◽  
...  

2021 ◽  
Vol 9 ◽  
Author(s):  
Pham Quy Giang ◽  
Tran Trung Vy

In developing countries in general and in Vietnam in particular, flood induced economic loss of agriculture is a serious concern since the livelihood of large populations depends on agricultural production. The objective of this study was to examine if climate change would exacerbate flood damage to agricultural production with a case study of rice production in Huong Son District of Ha Tinh Province, North-central Vietnam. The study applied a modeling approach for the prediction. Extreme precipitation and its return periods were calculated by the Generalized Extreme Value distribution method using historical daily observations and output of the MRI-CGCM3 climate model. The projected extreme precipitation data was then employed as an input of the Mike Flood model for flood modeling. Finally, an integrated approach employing flood depth and duration and crop calendar was used for the prediction of potential economic loss of rice production. Results of the study show that in comparison with the baseline period, an increase of 49.14% in the intensity of extreme precipitation was expected, while the frequency would increase 5 times by 2050s. As a result, the seriousness of floods would increase under climate change impacts as they would become more intensified, deeper and longer, and consequently the economic loss of rice production would increase significantly. While the level of peak flow was projected to rise nearly 1 m, leading the area of rice inundated to increase by 12.61%, the value of damage would rise by over 21% by 2050s compared to the baseline period. The findings of the present study are useful for long-term agricultural and infrastructural planning in order to tackle potential flooding threats to agricultural production under climate change impacts.


2021 ◽  
Author(s):  
Sara Minoli ◽  
Jonas Jägermeyr ◽  
Senthold Asseng ◽  
Christoph Müller

<p>Broad evidence is pointing at possible adverse impacts of climate change on crop yields. Due to scarce information about farming management practices, most global-scale studies, however, do not consider adaptation strategies.</p><p>Here we integrate models of farmers' decision making with crop biophysical modeling at the global scale to investigate how accounting for adaptation of crop phenology affects projections of future crop productivity under climate change. Farmers in each simulation unit are assumed to adapt crop growing periods by continuously selecting sowing dates and cultivars that match climatic conditions best. We compare counterfactual management scenarios, assuming crop calendars and cultivars to be either the same as in the reference climate – as often assumed in previous climate impact assessments – or adapted to future climate.</p><p>Based on crop model simulations, we find that the implementation of adapted growing periods can substantially increase (+15%) total crop production in 2080-2099 (RCP6.0). In general, summer crops are responsive to both sowing and harvest date adjustments, which result in overall longer growing periods and improved yields, compared to production systems without adaptation of growing periods. Winter wheat presents challenges in adapting to a warming climate and requires region-specific adjustments to pre and post winter conditions. We present a systematic evaluation of how local and climate-scenario specific adaptation strategies can enhance global crop productivity on current cropland. Our findings highlight the importance of further research on the readiness of required crop varieties.</p>


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