Session 2.7: Regional Integrated Assessments
For a complete list of all of the workshop abstracts click here (PDF).
If you are presenting a poster, please view the Poster Specifications here
Session 2.7: Oral Presentation
Title: Climate Change Impact on the productivity of crops in smallholder systems in West Africa: The case of Navrongo, Ghana and Nioro, Senegal
Authors: D. S. MacCarthy1, B. S. Freduah1, S.G.K. Adiku1, M. Ly2, S.B. Traore2,
S. Narh1, and P.S. Traore3 1 University of Ghana, College of Basic and Applied Sciences, School of Agriculture, Ghana, 2 Centre Regional AGRHYMET, Niger, 3 International Crop Research Institute for Semi-Arid Tropics, Mali
Abstract: Climate change is evident in Ghana with increasing temperatures and reducing annual seasonal rainfall amount which has become more erratic in almost all the agro-ecological zones. Farmers in Ghana are expected to be adversely affected by these changes in climate due to their reliance on rain-fed agriculture. Using two crop simulation models; Agricultural Production Systems Simulator (APSIM) and Decision Support Systems for Agro-Technological Transfer (DSSAT) these AgMIP tools were used to simulate cereal (maize and sorghum/millet) and groundnut yields for Navrongo, Ghana and Nioro, Senegal under current climate (1980 – 2009) and thereafter for 5 future climates (Mid Century time slice 2040 – 2069) under RCPs 4.5 and 8.5. Farmer survey data was used as input data on crop management practices and complemented with data from relevant literature to successfully initialize the two models and to represent observed yields from the survey. Generally, the 2 crop models agreed on the direction of change in yield. Uncertainty, however, remains in the magnitude of change as the models did not agree in this regard. Projected yield changes varied among GCMs and RCPs. Similarly, the magnitude of impact differed between the 2 locations with climate change impact being more severe in Nioro than in Navrongo. Methodologies employed in model initialization as well as the probable reasons for the differences in magnitude of model predictions will be discussed. The output of the models have been used to assess the impact of Climate change on the livelihoods of farmers in the two locations.
Session 2.7: Oral Presentation
Title: Reducing vulnerability to climate change in semi-arid Zimbabwe: a multi-model approach for redesigning smallholder farming futures
Authors: Sabine Homann-Kee Tui1, K. Descheemaeker2, P. Masikati3, G. Sisito4, R.
Valdivia5, O. Crespo6, B. Francis7, L. Claessens1..
1 International Crops Research Institute for the Semi-arid Tropics (ICRISAT), Zimbabwe, 2 Plant production systems, Wageningen University, The Netherlands, 3 World Agroforestry Centre (ICRAF), Lusaka, Zambia, 4 Matopos Research Institute, Zimbabwe, 5 Oregon State University, USA, 6 University of Capetown, South Africa, 7 Institute of Development Studies, Zimbabwe
Abstract: Climate change will impact productivity and food security of maize-based crop-livestock systems in semi-arid Zimbabwe. Earlier results from testing climate change adaptation options in Nkayi district showed that incremental technology-based changes are insufficient for improving smallholder livelihoods affected by climate change. In this paper we therefore explored requirements and potential impacts of transformative systems redesign. Scientists and stakeholders engaged in new thinking on drivers and opportunities for supporting desirable change, assuming that improved access to inputs, knowledge and markets would encourage smallholders adopting improved practices and technologies. Significant improvements in productivity, food security and income was achieved through a combination of larger cultivated areas, more diverse food and forage crops, integrated soil fertility management, small-scale mechanization, integrated with more livestock and improved livestock management. Food security oriented adaptation packages focusing on small ruminants and expanding groundnut and sorghum production
would provide 76% increases in net returns for extremely poor farms. Market-oriented livestock production with improved fodder and manure management would increase the net returns of poor and non-poor farms up to 85%. More than a third would however persistent below minimum living standards, indicating the need for opportunities outside agriculture. Translating innovations into multi-modeling frameworks enabled comparison of purely climate and socio-economic effects and informed adaptation strategies at farm and larger scales. This approach supports policy decisions, illustrating that future socio-economic conditions and policies are key factors for reducing vulnerability and create sustainable futures for areas like semi-arid Zimbabwe.
Session 2.7: Oral Presentation
Title: Impact of climate change on irrigated maize in Tamil Nadu, Southern India
Authors: Geethalakshmi Vellingiri, R. A. Palanisamy, B. Kulanthaivel, G. Ramasamy, S.P. McDermid, M. Narayanasamy, L. Arunachalam, S. Kandasamy, V. Krishnaraj, A. Krishnamoorthi, K. Sembanan, P. Shanmugam.
Agro Climate Research Centre, Tamil Nadu Agricultural University, India
Abstract: Climate change is one of the most pressing global problems; the impacts are felt by the human society on regional to local scales. Here, we quantify the impact of climate change on maize in the Trichy district in Tamil Nadu, India through integrated climate-crop
assessment utilizing the Protocols developed under Agricultural Model Inter-comparison and Improvement Project (AgMIP). Baseline climate data (1980-2010) was obtained from the TNAU agro-meteorological observatory. Future climate projections were taken from five global climate models obtained from the Coupled Model Inter-comparison Project (CMIP5) database, which were selected to represent the spread of uncertainty in climate projections from the full GCM ensemble. To simulate crop yields over both historical and future climate time scales, we utilized the Decision Support System of Agrotechnology Transfer (DSSAT) and the Agricultural Production Systems Simulator (APSIM) crop system models. Both DSSAT and APSIM model simulations under RCP 8.5 climate conditions (without adaptation) predicted decreased maize yields, with varied magnitude, for all the five models. Forcing CanESM2 (Hot-Wet condition compared to other models), inmcm4 (Hot-dry), IPSL-CM5A-MR (Cool-wet), MIROC 5 (Cool-dry) and HadGEM2 (Middle condition) models with APSIM showed a deviation in maize productivity by (-)8 to (+5), (-)3 to (+)7, (-) 2 to (-)8, (-)4 to (+)14.6 and (-)6 to (+)9 per cent respectively. While with DSSAT forcing, CanESM2, inmcm4, IPSL-CM5A-MR, MIROC 5 and HadGEM2 models showed (-)11 to (-)22, (-)2 to (-)7 , (-)10 to (-)33, (-)2 to (-)7 and (-)8 to (-)19 per cent deviation in maize productivity compared to baseline yield.
Session 2.7: Oral Presentation
Title: Assessment of Climate sensitivity to present production system and evaluation of use of adaptation strategies to improve the livelihood security of small and marginal farmers of Indo-Gangetic Plains of India – A multi-crop-climate-economic modeling approach
Authors: Nataraja Subash1, H. Singh1, A.S. Panwar1, M. S. Meena2, S. V. Singh3, B. Singh4, G. P. Paudel5, G. Baigorria6, A. C. Ruane7, S. P. McDermid8, K. Boote9, P. Paulton10,
C. Porter9, R. O. Valdivia11, C. Rosenzweig7, J. W. Jones9, J. M. Antle11, and C. Mutter12
1 ICAR-IIFSR, 2 ICAR-ATARI, 3 ICAR-NDRI, 4 CIMMYT, 5 TNAU, India, 6 University of Nebraska-Lincoln, USA, 7 NASA Goddard Institute for Space Studies, USA, 8
New York University, USA, 9 University of Florida, USA, 10 CSIRO, Australia, 11 Oregon State University, USA, 12Columbia University, USA
Abstract: Indo-Gangetic Plains, the food basket of India experienced climatic variability/fluctuations, occurrence of extreme events during last 30-years at one or other places and affected the current production system and thereby food and nutritional security and livelihoods of small and marginal farmers, which contributes 80 % of the farming population. Even though the green revolution brought irrigation/infrastructural development in this region, the spatial and temporal variability of monsoon, occurrence of cold waves, heat waves, unusual occurrence of rainfall during different pheno phase of the crop determines the final productivity. Under Agricultural Modeling intercomparison and Improvement Project, we have studied the climate sensitivity to present production system and also evaluated the adaptation strategies to improve the productivity in Indo-Gangetic plains of India. The AgMIP Phase II protocols were used to study the linking of climate-crop-economic models for two study sites Meerut and Karnal. At Meerut, the decadal trends in meteorological parameters showed significant decreasing trend of June rainfall of the order of_50.3 mm/decade; this may affect the level of groundwater. This leads to higher input costs. In the case of wheat, a significant increasing trend of maximum temperature during March and April could be one of the reasons for decreasing wheat grain yields. The increasing trend of maximum temperature during the milking/dough stage may result in forced maturity, which decreases the grain size and thereby grain
yield. The adaptation strategies such as use of short duration varieties in rice and wheat, advancement of date of sowing in wheat etc along with conservation measures were identified after discussion with local stakeholders, which increases the productivity and thereby increases the livelihood security of small and marginal farmers of the Indo-Gangetic plains of India.
Session 2.7: Oral Presentation
Title: Evaluating Crop Models for Use with Economic Models in Integrated Assessment
Authors: John Antle1, L. Claessens2, S. Gummadi3, R. Valdivia1, H. Zhang1, C. Dixon1
1 Oregon State University, USA, 2 ICRISAT Kenya, 3 ICRISAT Ethiopia
Abstract: Crop simulation models are used in integrated assessments to quantify the effects of changes in climate and management on crop productivity. They can be used to represent these effects using point data (e.g., matched to spatial coordinates of farm survey data) and with data averaged over a spatial unit and over time (e.g., gridded data). Economists are using crop models as well as observational data to represent the spatial heterogeneity in crop productivity as well as the temporal variation. The crop models may be used to represent changes in absolute or relative productivity. Crop modelers typically evaluate the performance of crop simulation models using several criteria, including probability of exceedance graphs and plots of observed versus actual yields. The AgMIP Regional Integrated Assessment Handbook summarizes these methods and also suggests ways that crop models can use farm survey data in implementation of impact and adaptation analysis. This paper uses examples from recent research to critically evaluate these methods, the types of data that are being used, and the adequacy of these methods and data for carrying out integrated assessments.
Session 2.7: Oral Presentation
Title: Future Climate Change and Its Effect on Maize Yields in Selected Semi-arid Areas of Southern Africa
Authors: Weldemichael Tesfuhuney1 , O. Crespo4 , T. Mpuisang2 , M.Teweldemedhin7 , P. Gwimbi9 , W. Durand3 , Y. Beletse3 , M. Jones6 , S. Walker1,4 , and D. Cammarano5
1 University of the Free State, South Africa, 2 Botswana College of Agriculture, Botswana, 3 Agricultural Research Council, South Africa, 4 University of Cape Town, South Africa, 7 Polytechnic of Namibia, 5 National University of Lesotho, Lesotho, 5 The James Hutton Research Institute, Scotland, 4 University of Nottingham, UK
Abstract: Climate change impact projections in countries of Southern Africa (SADC) reveal increased occurrences and severity of drought. AgMIP creates a robust framework to understand the climate impact on main stable crops. The aim of the study was to assess and compare historical and future maize (Zea mays L.) yield simulations using APSIM with historical (1980-2010) and mid-century future (2040-2070) climate scenarios on 12 selected semi-arid areas of SADC. Tools and protocols developed for crop modelling in AgMIP were adopted to address improved projections of climate impacts on smallholder maize production with and without adaptation. Summary results of yield simulations
Countries in SADC Sites No. of Farmers Performance Historic (t ha-1) Mean Future Scenarios (t ha-1) with Adaptation R2 RMSE CCSM GFDL HADG MICR MPI Mean South Africa 3 2254 0.53 624 1.44 1.63 1.54 1.75 1.63 1.35 1.58 Botswana 1 30 0.59 355 0.49 0.75 0.74 0.80 0.92 0.35 0.71 Namibia 6 467 0.46 639 0.27 0.39 0.42 0.49 0.52 0.62 0.49 Lesotho 2 30 0.65 548 0.66 1.04 0.84 0.70 0.69 0.80 0.81. Model performance showed reasonable values, when only district yields were used for testing, in spite of climate related year-to-year variations. Future climate change shows variability in yields, that makes maize production more risky without improved adaptations. Diversified potential management practices due to climate change would improve rural livelihoods. Thus, current maize production system is sensitive to climate change, giving a negative impact of climate change on future maize production in the semi-arid areas of SADC region.
46. Poster Presentation: Session 2.7
Title: The Regional Gridded Crop Modelling Activity (RGCMA) – India Focus
Authors: Delphine Deryng1, C. Deva2, J. Elliott1 1 University of Chicago & NASA GISS, USA, 2 University of Leeds, UK
Abstract: We present the first of a new set of regional activities designed to apply gridded crop modelling methods over large areas using high resolution climate and agricultural inputs to improve and harmonise impact, adaptation and vulnerability (IAV) assessments across
regions. Agriculture in India undergoes dramatic groundwater resources depletion. Projected climate change is expected to impact both water resources for irrigation and crop yield. This Regional Gridded Crop Modelling Activity (RGCMA) activity aims to explore the interaction between irrigated crop production and ground water resources in India under climate change using an ensemble of gridded crop models and high resolution regional climate and agricultural datasets. The overall motivation, objectives and methods will be presented; interaction with and complementarity to other regional and global
AgMIP activities will be discussed.
47. Poster Presentation: Session 2.7
Title: Assessing climate impacts to critical indicators of farmers’ livelihood: a carbon, temperature, and water sensitivity analysis
Authors: Sonali McDermid1 , R. Valdivia2 , A. C. Ruane3 , J. Antle2 , and D. Murthy4
1 New York University, USA, 2 Oregon State University, USA, 3 NASA GISS, USA, 4 ICRISAT, India
Abstract: The AgMIP Coordinated Climate-Crop Modeling Project (C3MP) engages the global crop modeling community to explore crop and model sensitivities to changes in carbon dioxide concentrations ([CO2]), temperature (T), and precipitation (W) utilizing a standardized set of experiments and analysis protocols. Modelers run their calibrated crop models over a 30-year period, modifying their weather data to include a range of CTW changes from 99 prescribed sensitivity tests. A series of Impacts Response Surfaces (IRS) are created from the resulting yield changes to explore the modeled sensitivity to the CTW uncertainty space. These IRS allow researchers to expediently estimate CTW impacts, such as those associated with increasing [CO2] and T while holding W constant (a proxy for climate change conditions). While C3MP methods have been primarily used to evaluate crop sensitivities to CTW changes, we now expand this approach to evaluate these impacts on key economic indicators: poverty rates, vulnerability, and farm income. This assessment is conducted for 90 rainfed maize farmers in Andhra Pradhesh, India, and each farm has been modeled for 30 years with their respective management conditions. For each farm, the relative yield changes from the baseline are computed for each sensitivity test, and serve as one farm system input in the TOA-MD modeling framework. For each of the specified economic indicators, the mean response to each sensitivity test is assessed across the population of farms. From this mean response, IRS are constructed to analyze the indicators’ sensitivity to CTW changes, and identify critical thresholds of farm livelihood impacts.
48. Poster Presentation: Session 2.7
Title: Spatial analysis of profitability of Chickpea farms under changing climate in Andhra Pradesh, India
Authors: Swamikannu Nedumaran1, K. D. Murthy1, D. K. Charyulu1, and M. K. Gumma
1 ICRISAT, India
Abstract: Chickpea (Cicer arietinum L.) is the largest pulse crop grown in India and the second largest food legume in the world. In India, the growth rate of chickpea area is highest in Andhra Pradesh state followed by Karnataka, Maharashtra and Madhya Pradesh between 1970 and 2010. In the last two decades, chickpea become a major pulse crop grown under rainfed condition in semi-arid tropics of Andhra Pradesh. The fallow-chickpea cropping systems covers more than 70% of the cropped area which replaced crops such as sorghum, sunflower, coriander and groundnut mainly because of higher returns of chickpea. But in the recent years, Andhra Pradesh semi-arid region is facing frequent drought and erratic distribution of rainfall which leads to crop failure and loss to the chickpea farmers. The objective of this paper is to assess the spatial variation and sensitivity of gross margins of chickpea farms under changing climate scenarios and also evaluate different climate-resilient adaptation options to improve farm profits. The chickpea growing area in the state was delineated using LANDSAT-8 & MODIS 250 m temporal data with spectral matching techniques. The current and future climate data was used to simulate the chickpea yields using the DDSAT crop system model with farm level management input data collected from 810 spatially heterogeneous representative chickpea farmers from 4 districts of Andhra Pradesh. The farm gate chickpea price and plot level cost of production was used to estimate the spatial variation in gross margins of chickpea farms in four main chickpea growing districts of Andhra Pradesh. The results suggested that the gross margins of chickpea farms differ regionally and needs location specific adaptation options to increase the profitability of chickpea farms. These results will be used to identify the hotspot of chickpea cultivation in the region and to design targeted adaptation options for higher impacts.
49. Poster Presentation: Session 2.7
Title: Uncertainty of GCM projections under different Representative Concentration Pathways (RCPs) at different temporal and spatial scales – Reflections from 4 sites in Indo-Gangetic Plains of India
Authors: Nataraja Subash1 , H. Singh1 , A. C. Ruane2 , S. McDermid3 , and G.A. Baigorria4
1 ICAR-Indian Institute of Farming Systems Research, India, 2 NASA-GISS, USA, 3 New York University, USA, 4 University of Nebraska-Lincoln, USA, *Corresponding
author: email@example.com, firstname.lastname@example.org
Abstract: The Projected climate change scenarios are one of the important input variables
along with well calibrated crop models for simulating productivity and the uncertainty in Climate change projections sometimes create error in food security projections. Even though the understanding and modeling of climate change has advanced significantly in recent decades, however, the daily projections of maximum and minimum temperature and rainfall by some GCMs still provides biased results in Indian Subcontinent, particularly for specific locations. These GCMs projected somewhat accurate and same projections
for larger regions. Under AgMIP (Agricultural Modeling Intercomparison and Improvement Project), we have analysed 29 CMIP5 GCMs under RCP4.5 and 8.5 under near-term (2010-2039), mid-term (2040-2069) and end of the century (2070-2099) for 6 sites in Indo-Gangetic Plains of South Asia. The method followed is mean and variability delta scenarios. The complete methodology followed is explained in AgMIP quick guide to climate scenarios. There is lot of uncertainty involved in subset of GCM selection, which will go to the crop modelers and economic modelers for integrated assessment. Some GCMs showed biasness to explain the projected rainfall scenarios, even in annual/monsoon season with more than 200 % change in projected rainfall. The GCMs which projected more than +200 % change in projected rainfall removed from the methodology and compared which GCMs fall under different quadrants. For eg. We have analyzed 29 GCMs for Meerut District during mid-term century (2040-2069) for RCP4.5 and RCP8.5. Out of 29 GCMs, 6 GCMs (CSIRO-MK3-6.0, IPSL-CM5A-MR, FGOALS-g2, IPSL-CM5B-LR, GISS-E2-R & GISS-E2-H) found biased which projected >200 % annual
rainfall. The scatter plotter diagram of 23 GCMs used to identify the GCMs under hot/wet, cool/wet, cool/dry, hot/dry and median quadrants. The GCMs close to mean value were chosen for further analysis.
50. Poster Presentation: Session 2.7
Title: AgMIP-Peru: An Initiative to Assess Climate Change Impacts on Agricultural Systems in Andean Ecosystems: Peruvian Andes as Pilot Site.
Authors: Irene Trebejo1 and R. Valdivia2n1 SENAMHI, Peru, 2 Oregon State University, USA
Abstract: Experts from public and private institutions in Peru have formed a multi-disciplinary
team under the coordination of the National Meteorological and Hydrological Service (SENAMHI) to assess the impacts of climate change on agricultural production systems in the Peruvian Andes. The pilot site is the region of Puno, situated at above 3,800 meters above sea level, is one of the most vulnerable regions in Peru. Changing climate and climate variability may worsen the already high levels of rural poverty and food insecurity.
The goal is to use the AgMIP Regional Integrated Assessment methods and tools as a proof of concept to build capacity and demosntrate stakeholders and policy makers about the kinds of information this type of assessments can provide.
While the production systems in the Andes are complex crop-livestock systems, this “Fast-Track” analysis will focus on potato based systems. Potato is one of the main stapples in this region and it is also one of the most vulnerable crops to changes in temperature (e.g. frost) and precipitation as well as pest and diseases. This poster will present all the background information about the pilot site and it will also serve as a report of all the previous activities carried out by AgMIP-Peru. AgMIP-Peru in coordination with AgMIP has
conducted 3 workshops between 2013 and 2015 to introduce the AgMIP framework and tools to scientsts from Peru, Ecuador, Chile and Colombia. AgMIP-Peru is also leading the AgMIP-Latin America coordination.
51. Poster Presentation: Session 2.7
Title: In Search of Sustainable Development: Modeling Semi-Subsistence Crop-Livestock Systems to Solve the Poverty-Productivity-Sustainability Puzzle in Sub-Saharan Africa
Authors: Roberto Valdivia1 , J. Antle1 , and J. Stoorvogel2
1 Oregon State University, USA, 2 Wageningen University, Netherlands
Abstract: Achieving the goal of sustainable development in African agriculture will require better understanding of the poverty-productivity-sustainability puzzle: why high poverty and resource degradation levels persist in African agriculture, despite decades of policy interventions and development projects. We hypothesize that the answer to this puzzle lies, at least in part, in understanding and appropriately analyzing key features of semi-subsistence crop-livestock systems typical of Sub-Saharan Africa: high degree of bio-physical and economic heterogeneity, complex and diversified production system involving a combination of subsistence and cash crops with livestock. We describe an integrated modeling approach designed to incorporate these features. We illustrate how this approach can be implemented to quantify economic and sustainability indicators for policy tradeoff analysis in the Machakos region, Kenya. The analysis suggests that a successful implementation of the Vision 2030 strategy of the Kenyan Government could lead to a sustainable development pathway and achieve newly proposed Sustainable Development Goals