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Tag: modeling

Pramod Aggarwal

Pramod Aggarwal leads the South Asia Regional Program for the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).

He earned his post-doctoral degree at the International Rice Research Institute, Philippines, and holds two doctoral degrees from the University of Indore, India, and from Wageningen University-Netherlands. He was awarded Academy of Sciences for the Developing World’s Ernesto Illy Trieste Science Prize in 2009, and the Indian National Science Academy’s Young Scientist Medal in 1983.

His professional research focuses on crop growth models for tropical environments, impact assessment of climatic variability and climate change on crops, and adaptation strategies and mitigation options, among other topics.

L.M. Suresh

L.M. Suresh leads CIMMYT’s maize pathology efforts in sub-Saharan Africa. He regularly contributes to Global Maize Program projects that have strategic significance in maize pathology, disease diagnosis, epidemiology and disease resistance.

Suresh also works on maize lethal necrosis (MLN) phenotyping with public and private partnership at CIMMYT and the Kenya Agricultural and Livestock Research Organization’s (KALRO) joint research station in Naivasha, Kenya. His team has phenotyped around 200,000 maize germplasm from various partners and 19 MLN resistant/tolerant hybrids have been released in east Africa so far. He has supported the training of more than 5000 researchers, students, extension workers, private seed company executives and farmers in rapid disease diagnosis and his contributions have helped to prevent further MLN spread throughout eastern and southern Africa.

Jelle Van Loon

Jelle Van Loon is an agricultural engineer with a PhD in biosystems modelling, and over a decade of experience in agricultural research for development in Latin America. He currently serves as Associate Director for Latin America of CIMMYT’s Sustainable Agrifood System Program, leading research initiatives aimed at building pathways towards resilient food systems and long-term rural development. Leading the innovations for development team, he coordinates a transdisciplinary team, including aspects like farmers market linkages and responsible sourcing, capacity development, and community-based outreach and explores the multiple interfaces between adaption, adoption and scaling from a socio-technical viewpoint in research for agricultural development.

In addition, Jelle has ample expertise in scale-appropriate mechanization from smallholder farm solutions to precision agriculture applications, has actively progressed to work in innovation systems thinking, and in addition he serves CIMMYT as representative for Latin America in which he focusses this line of work to establish impactful partnerships and innovative business models.

 

 

 

 

 

 

 

Diego Noleto Luz Pequeno

Diego Pequeno is a wheat crop modeler based within the Sustainable Agrifood Systems (SAS) program. He also works on a number of projects in collaboration with CIMMYT’s Global Wheat Program and a number of external organizations.

His work focuses mainly on the simulation of trait impact scenarios to guide breeding towards the most effective traits and trait combinations for global wheat production. He also works to determine the importance of a single trait or the best combination of traits under different climate change scenarios for different cropping systems in key wheat growing regions. He uses high-performance computer clusters to run gridded crop model simulations for current and future climate scenarios on a global scale.

Jordan Chamberlin

Jordan Chamberlin is a CIMMYT Spatial Economist based in Kenya. He holds a PhD in Agricultural Economics from Michigan State University and an MA in Geography from Arizona State University.

He conducts applied research on smallholder farm households, rural development and policies designed to promote welfare and productivity improvements.

Wei Xiong

Wei Xiong is an interdisciplinary researcher focusing on the interactions between agricultural production and environment, with specific experiences in climate change and agriculture, development of agricultural system modeling tools, evaluation of climate-smart agriculture, and Genotype by Environment Interaction analysis.

Xiong is good at using cutting-edge technologies (such as cloud computing, machine learning, big data, HPC, and bioinformatics) in G×E×M interaction analysis, with a track record of improving short- and long-term agricultural forecast models at the local, national, and global scales. He is also interested in smart agriculture, agricultural AI, and innovative predictive approaches from genomics to phenomics.

New publications: Role of Modelling in International Crop Research

“Crop modelling has the potential to significantly contribute to global food and nutrition security,” claim the authors of a recently published paper on the role of modelling in international crop research.  “Millions of farmers, and the societies that depend on their production, are relying on us to step up to the plate.”

Among other uses, crop modelling allows for foresight analysis of agricultural systems under global change scenarios and the prediction of potential consequences of food system shocks. New technologies and conceptual breakthroughs have also allowed modelling to contribute to a better understanding of crop performance and yield gaps, improved predictions of pest outbreaks, more efficient irrigation systems and the optimization of planting dates.

While renewed interest in the topic has led in recent years to the development of collaborative initiatives such as the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the CGIAR Platform for Big Data in Agriculture, further investment is needed in order to improve the collection of open access, easy-to-use data available for crop modelling purposes. Strong impact on a global scale will require a wide range of stakeholders – from academia to the private sector – to contribute to the development of large, multi-location datasets.

Resource-poor farmers worldwide stand to gain from developments in the field of crop modelling. Photo: H. De Groote/CIMMYT.
Resource-poor farmers worldwide stand to gain from developments in the field of crop modelling. (Photo: H. De Groote/CIMMYT)

In “Role of Modelling in International Crop Research: Overview and Some Case Studies,” CGIAR researchers outline the history and basic principles of crop modelling, and describe major theoretical advances and their practical applications by international crop research centers. They also highlight the importance of agri-food systems, which they view as key to meeting global development challenges. “The renewed focus on the systems-level has created significant opportunities for modelers to participant in enhancing the impact of science on developments. However, a coherent approach based on principles of transparency, cooperation and innovation is essential to achieving this.”

The authors call for closer interdisciplinary collaboration to better serve the crop research and development communities through the provision of model-based recommendations which could range from government-level policy development to direct crop management support for resource-poor farmers.

Read the full article in Agronomy 2018, Volume 8 (12).

Check out other recent publications by CIMMYT researchers below:

  1. A framework for priority-setting in climate smart agriculture research. 2018. Thornton, P.K., Whitbread, A., Baedeker, T., Cairns, J.E., Claessens, L., Baethgen, W., Bunn, C., Friedmann, M., Giller, K.E., Herrero, M., Howden, M., Kilcline, K., Nangia, V., Ramirez Villegas, J., Shalander Kumar, West, P.C., Keating, B. In: Agricultural Systems v. 167, p. 161-175.
  2. Cereal consumption and marketing responses by rural smallholders under rising cereal prices. 2018. Mottaleb, K.A., Rahut, D.B. In: Journal of Agribusiness in Developing and Emerging Economies v. 8, no. 3, p. 461-479.
  3. Community typology framed by normative climate for agricultural innovation, empowerment, and poverty reduction. 2018. Petesch, P., Feldman, S., Elias, M., Badstue, L.B., Dina Najjar, Rietveld, A., Bullock, R., Kawarazuka, N., Luis, J. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 131-157.
  4. Fit for purpose? A review of guides for gender-equitable value chain development. 2018. Stoian, D., Donovan, J.A., Elias, M., Blare, T. In: Development in Practice v. 28, no. 4, p. 494-509.
  5. Gendered aspirations and occupations among rural youth, in agriculture and beyond: a cross-regional perspective. 2018. Elias, M., Netsayi Mudege, Lopez, D.E., Dina Najjar, Kandiwa, V., Luis, J., Jummai Yila, Amare Tegbaru, Gaya Ibrahim, Badstue, L.B., Njuguna-Mungai, E., Abderahim Bentaibi. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 82-107.
  6. Genome-wide association study reveals novel genomic regions for grain yield and yield-related traits in drought-stressed synthetic hexaploid wheat. 2018. Bhatta, M.R., Morgounov, A.I., Belamkar, V., Baenziger, P.S. In: International Journal of Molecular Sciences v. 19, no. 10, art. 3011.
  7. Identificacion de areas potenciales en Mexico para la intervencion con maiz biofortificado con zinc = Identification of potential areas in Mexico for intervention with biofortified high-zinc maize. 2018. Ramirez-Jaspeado, R., Palacios-Rojas, N., Salomon, P., Donnet, M.L. In: Revista Fitotecnia Mexicana v. 4, no. 3, p. 327 – 337.
  8. Impact of climate-change risk-coping strategies on livestock productivity and household welfare: empirical evidence from Pakistan. 2018. Rahut, D.B., Ali, A. In: Heliyon v. 4, no. 10, art. e00797.
  9. Impact of conservation agriculture on soil physical properties in rice-wheat system of eastern indo-gangetic plains. 2018. Kumar, V., Kumar, M., Singh, S.K., Jat, R.K. In: Journal of Animal and Plant Sciences v. 28, no. 5, p. 1432-1440.
  10. Impact of ridge-furrow planting in Pakistan: empirical evidence from the farmer’s field. 2018. Hussain, I., Ali, A., Ansaar Ahmed, Hafiz Nasrullah, Badar ud Din Khokhar, Shahid Iqbal, Azhar Mahmood Aulakh, Atta ullah Khan, Jamil Akhter, Gulzar Ahmed. In: International Journal of Agronomy v. 2018, art. 3798037.
  11. Introduction to special issue: smallholder value chains as complex adaptive systems. 2018. Orr, A., Donovan, J.A. In: Journal of Agribusiness in Developing and Emerging Economies v. 8, no. 1, p. 2-13.
  12. Local dynamics of native maize value chains in a peri-urban zone in Mexico: the case of San Juan Atzacualoya in the state of Mexico. 2018. Boue, C., Lopez-Ridaura, S., Rodriguez Sanchez, L.M., Hellin, J. J., Fuentes Ponce, M. In: Journal of Rural Studies v. 64, p. 28-38.
  13. Local normative climate shaping agency and agricultural livelihoods in sub-Saharan Africa. 2018. Petesch, P., Bullock, R., Feldman, S., Badstue, L.B., Rietveld, A., Bauchspies, W., Kamanzi, A., Amare Tegbaru, Jummai Yila. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 108-130.
  14. Maize seed systems in different agro-ecosystems; what works and what does not work for smallholder farmers. 2018. Hoogendoorn, C., Audet-Bélanger, G., Boeber, C., Donnet, M.L., Lweya, K.B., Malik, R., Gildemacher, P. In: Food security v. 10, no. 4, p. 1089–1103.
  15. Mapping adult plant stem rust resistance in barley accessions Hietpas-5 and GAW-79. 2018. Case, A.J., Bhavani, S., Macharia, G., Pretorius, Z.A., Coetzee, V., Kloppers, F.J., Tyagi, P., Brown-Guedira, G., Steffenson, B.J. In: Theoretical and Applied Genetics v. 131, no. 10, p. 2245–2266.
  16. Potential for re-emergence of wheat stem rust in the United Kingdom. 2018. Lewis, C.M., Persoons, A., Bebber, D.P., Kigathi, R.N., Maintz, J., Findlay, K., Bueno-Sancho, V., Corredor-Moreno, P., Harrington, S.A., Ngonidzashe Kangara, Berlin, A., Garcia, R., German, S.E., Hanzalova, A., Hodson, D.P., Hovmoller, M.S., Huerta-Espino, J., Imtiaz, M., Mirza, J.I., Justesen, A.F., Niks, R.E., Ali Omrani., Patpour, M., Pretorius, Z.A., Ramin Roohparvar, Hanan Sela, Singh, R.P., Steffenson, B.J., Visser, B., Fenwick, P., Thomas, J., Wulff, B.B.H.,  Saunders, D.G.O. In: Communications Biology v. 1, art. 13.
  17. Qualitative, comparative, and collaborative research at large scale: an introduction to GENNOVATE. 2018. Badstue, L.B., Petesch, P., Feldman, S., Prain, G., Elias, M., Kantor, P. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 1-27.
  18. Qualitative, comparative, and collaborative research at large scale: the GENNOVATE field methodology. 2018. Petesch, P., Badstue, L.B., Camfield, L., Feldman, S., Prain, G., Kantor, P. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 28-53.
  19. Transaction costs, land rental markets, and their impact on youth access to agriculture in Tanzania. 2018. Ricker-Gilbert, J., Chamberlin, J. In: Land Economics v. 94, no. 4, p. 541-555.
  20. What drives capacity to innovate? Insights from women and men small-scale farmers in Africa, Asia, and Latin America. 2018. Badstue, L.B., Lopez, D.E., Umantseva, A., Williams, G.J., Elias, M., Farnworth, C.R., Rietveld, A., Njuguna-Mungai, E., Luis, J., Dina Najjar., Kandiwa, V. In: Journal of Gender, Agriculture and Food Security v. 3, no. 1, p. 54-81.

 

A Capacity Approach To Climate Change Modeling: Identifying Crop Management Adaptation Options

Crop growth simulation models coupled with climate model projections are promoted and increasingly used for assessing impacts of climate change on crop yields and for informing crop-level adaptations. However, most reported studies are unclear regarding the choice of the global circulation models (GCMs) for climate projections and the corresponding uncertainty with these type of model simulations.In our study, we investigated to what extent far climate-change modeling can be used for identifying crop management adaptation options to climate change. We focused our analysis on a case study of maize production in southern Africa using the APSIM crop growth model (Agricultural Production Systems sIMulator) and projections from 17 individual climate models for the period 2017-2060 for the contrasting representative concentration pathways 2.6 and 8.5.

Our findings demonstrate that the identification of crop management-level adaptation options based on linked climate-crop simulation modelling is largely hindered by uncertainties in the projections of climate change impacts on crop yields. With uncertainties in future crop yield predictions of around 30 to 40% or more, many potential adaptation options to climate change are not identifiable or testable with crop-climate models.

First, the variation of climate predictions is high. Their accuracy is limited by fundamental, irreducible uncertainties that are the result of structural differences in the GCMs as well as different model parametrization and downscaling approaches. We found that different GCMs gave largely different results, without any clear pattern.

Second, there is also large uncertainty in simulating the responses of crops to changing climate because of the different structures, and input data and parameters of crop models. Besides, crop models often lack key processes (e.g., physiological plant responses to extreme temperatures) related to climate change impacts, as they were not built for this purpose. It is also evident that due to the limited capability of crop models in simulating effects of soil and crop management practices on crop yields, only a limited number of adaptation options could be informed.

A more successful approach for informing adaptation to climate change may be to begin with the decision-making context, assessing the existing capacities and vulnerabilities of farmers and their communities to climate change. This “capacity approach” does not require probability-based estimates of future climate, but rather a range of plausible representations that can help to better understand how the climate-related vulnerabilities can be addressed. Most of the decisions on crop management are made by the farmer in the context of his/her production objectives and farming opportunities and constraints. From there, farming options can be identified and proposed that are feasible and robust over a range of plausible climatic futures, without the need for detailed climate projections.

Furthermore, adaption to climate change is also entwined with socioeconomic drivers, such as globalization, economic and political priorities, and demographics. In fact, complexities in economic and social systems may outweigh climatic uncertainties in determining possible and feasible adaptation options. A general trend observed is that by diversifying their income sources, including off-farm income, farmers become less vulnerable to climate variability and change.

Whilst we argue that results from GCMs cannot be directly used for informing local-scale adaptation options, we do acknowledge that the use of ensembles of both climate and crop models in regionally- and globally-oriented impact studies can provide valuable information that can guide policy decision-making on agricultural adaptation to climate change at national and international scales.

These findings are described in the article entitled Can we use crop modelling for identifying climate change adaptation options? recently published in the journal Agricultural and Forest Meteorology. This work was conducted by Marc Corbeels, David Berre, Leonard Rusinamhodzi and Santiago Lopez-Ridaura from the International Maize and Wheat Improvement Center (CIMMYT) and the French Agricultural Research Centre for International Development (CIRAD).

This blog was originally published on the website Science Trends, find it here.

Santiago Lopez-Ridaura

Santiago Lopez-Ridaura focuses on the quantitative analysis of agricultural systems at the field, farm, landscape and regional level. By developing and applying a suit of quantitative systems analysis approaches, methods and tools, he builds a detailed understanding of the characteristics, dynamics and diversity of farming systems in a given region. Then, through multi-criteria assessments of different cropping and farming systems, he helps target interventions to specific types of farms within certain agro-ecologies.

Lopez-Ridaura works closely with farmers, farmer organizations, national and international non-governmental organizations, and agricultural research and development institutions to help them answer what technological and policy interventions are most appropriate for a given community. This enables organizations to comprehensively understand the main challenges and opportunities of specific technologies, and improve their adoption and adaptation to reach impact at scale.

Matthew Reynolds

Matthew Reynolds develops and transfers technologies to increase productivity of wheat cropping systems worldwide, primarily focusing on less developed countries. At CIMMYT he’s helped create a new generation of advanced lines based on physiological breeding approaches to widen the wheat genepool, increase understanding of yield potential and adaptation of wheat to drought and heat stress, develop high throughput phenotyping methodologies and train other researchers.

To further these goals, Reynold’s is developing global collaborations to tap into the expertise of plant scientists worldwide, such as the International Wheat Yield Partnership, and coordinates the formation of the Heat and Drought Wheat Improvement Consortium. He’s also leading the community of practice on crop modeling for the CGIAR Big Data platform.

Reynolds has published widely in the area of crop physiology and genomics, and mentored graduate students through affiliations with universities worldwide.

Kai Sonder

Kai Sonder is currently the Geographic Information System (GIS) Laboratory Manager. The unit provides spatial data and analysis, targeting and foresight work and agro meteorology to the organization. It also provides training on GIS to all of CIMMYT’s scientists and projects, as well as partners applied to development-oriented agricultural research on maize, wheat and conservation agriculture in developing countries in Africa, Asia and Latin America.

New Publications: New environmental analysis method improves crop adaptation to climate change

EL BATAN, Mexico (CIMMYT) – A new paper proposes researchers analyze environmental impacts through “envirotyping,” a new typing method which allows scientists to dissect complex environmental interactions to pinpoint climate change effects on crops. When used with genotyping and phenotyping – typing methods that assess the genetic and in-field performance of crops – researchers can more effectively adapt crops to future climates.

Climate change has significantly shifted weather patterns, which affects a number of farming conditions such as less reliable weather, extreme temperatures and declining soil and water quality. These extreme conditions bring a number of unexpected stresses to plants such as drought and new pests.

How a crop performs is largely dependent on the environment where it grows, making it crucial for breeders to analyze crops in growing areas. However, many breeding tools such as genetic mapping are based on the environment where phenotyping is performed, and phenotyping is often conducted under managed environmental conditions.

Envirotyping allows researchers to apply real-world conditions when assessing the performance of crops. It has a wide range of applications including the development of a four-dimensional profile for crop science, which would include a genotype, phenotype, envirotype and time.

Currently, envirotyping requires environmental factors to be collected over the course of multiple trials for use in contributing to crop modeling and phenotypic predictions. Widespread acceptance of this new typing method could help establish high-precision envirotyping, as well as create highly efficient precision breeding and sustainable crop production systems based on deciphered environmental impacts.

Read the full study “Envirotyping for deciphering environmental impacts on crop plants.” and check out other recent publications from CIMMYT staff below.

 

  • Effects of nitrogen fertilizer and manure application on storage of carbon and nitrogen under continuous maize cropping in Arenosols and Luvisols of Zimbabwe. Mujuru, L., Rusinamhodzi, L., Nyamangara, J., Hoosbeek, M.R. In: Journal of Agricultural Science, v. 154, p. 242-257.

 

  • Empirical evaluation of sustainability of divergent farms in the dryland farming systems of India. Amare Haileslassie, Craufurd, P., Thiagarajah, R., Shalander Kumar, Whitbread, A., Rathor, A., Blummel, M., Ericsson, P., Krishna Reddy Kakumanu In: Ecological indicators, v. 60, p. 710-723.

 

  • Evaluation of tillage and crop establishment methods integrated with relay seeding of wheat and mungbean for sustainable intensification of cotton-wheat system in South Asia. Choudhary, R., Singh, P., Sidhu, H.S., Nandal, D.P., Jat, H.S., Singh, Y., Jat, M.L. In: Field Crops Research, v. 199, p. 31-41.

 

  • Fertilizers, hybrids, and the sustainable intensification of maize systems in the rainfed mid-hills of Nepal. Devkota, K.P., McDonald, A., Khadka, L., Khadka, A., Paudel, G., Devkota, M. In: European Journal of Agronomy, v. 80, p. 154-167.

 

  • Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches. Pasam, R.K., Bansal, U., Daetwyler, H.D., Forrest, K.L., Wong, D., Petkowski, J., Willey, N., Randhawa, M.S., Chhetri, M., Miah, H., Tibbits, J., Bariana, H.S., Hayden, M. In: Theoretical and Applied Genetics, v. 130, no. 4, p. 777-793.

 

  • Diallel analysis of acid soil tolerant and susceptible maize inbred lines for grain yield under acid and non-acid soil conditions. Mutimaamba, C., MacRobert, J.F., Cairns, J.E., Magorokosho, C., Thokozile Ndhlela, Mukungurutse, C., Minnaar-Ontong, A., Labuschagne, M. In: Euphytica, v. 213, no. 88, p.1-10.

 

  • Direct Nitrous Oxide emissions from Tropical And Sub-Tropical Agricultural Systems: a review and modelling of emission factors. Albanito, F., Lebender, U., Cornulier, T., Sapkota, T.B., Brentrup, F., Stirling, C., Hillier, J. In: Nature Scientific reports, v. 7, no. 44235.

 

  • Dissection of a major QTL qhir1 conferring maternal haploidinduction ability in maize. Nair, S.K., Molenaar, W., Melchinger, A.E., Prasanna, B.M., Martinez, L., Lopez, L.A., Chaikam, V. In: Theoretical and Applied Genetics, v. 130, p. 1113-1122.

 

  • Effect of the few-branched-1 (Fbr1) tassel mutation on performance of maize inbred lines and hybrids evaluated under stress and optimum environments. Shorai Dari, MacRobert, J.F., Minnaar-Ontong, A., Labuschagne, M. In: Maydica, vol. 62, p. 1-10.

 

Breaking Ground: Crop simulation models help Balwinder Singh predict future challenges

TwitterBGBalwinder3Breaking Ground is a regular series featuring staff at CIMMYT

EL BATAN, Mexico (CIMMYT) – Balwinder Singh uses crop simulation models to help smallholder farmers in South Asia prepare for future climates and unexpected challenges.

Despite improvements in agricultural technology in the past few decades, crop yield gaps persist globally. As climate patterns change, farmers are at risk of crop loss and reduced yields due to unforeseen weather events such as drought, heat or extreme rains.

Singh, a cropping system simulation modeler at the International Maize and Wheat Improvement Center (CIMMYT) based in New Delhi, India, uses crop simulation models—software that can estimate crop yield as a function of weather conditions, soil conditions, and choice of crop management practices—to develop future climate predictions that can help farmers reduce risk, overcome labor and resource constraints, intensify productivity and boost profitability.

“Using future climate data, simulation modelling allows researchers to develop hypotheses about future agricultural systems,” said Singh. “This can help predict and proactively mitigate potentially catastrophic scenarios from challenges such as shrinking natural resources, climate change and the increasing cost of agricultural production.”

A specific focus is on how to best quantify, map and diagnose the causes of the gap between potential yields and actual yields achieved by cereal farmers in the Indo-Gangetic Plain. “My research combines field experimentation, participatory engagement, and cropping systems modelling and spatial data to identify promising technologies for increasing crop productivity and appropriate geographical areas for out scaling,” he said.

For example, Singh and a team of scientists have used simulation tools to find out why wheat productivity is low in the Eastern Gangetic Plains, for example, late sowing, suboptimal crop mangement and terminal heat stress. This process identified various potential techniques to raise wheat productivity, such as early sowing, zero tillage, or short duration rice varieties to facilitate early harvest and field vacation. Geospatial data and tools were used to identify the potential target zones for deployment of these promising technologies.

“The research is helping farmers increase agricultural productivity and to manage climate-related crop production risk and increase the use of agricultural decision support systems,” Singh said. “My research towards improving cereal production systems in South Asia contributes to the knowledge, process understanding and modelling tools needed to underpin recommendations for more productive and sustainable production systems.”

Growing up in rural India in a farming family, Singh viewed firsthand the uncertainty that smallholder farmers can face.

“I was brought up and studied in northwestern India – the region where the green revolution occurred known as the food basket of India,” Singh said.

“I grew up playing in wheat and cotton fields, watching the sowing, growing and harvesting of crops, so an interest in agricultural science came naturally to me and I have never regretted choosing agriculture as a career.”

While studying for his bachelor’s and master’s degrees in agronomy at Punjab Agricultural University (PAU) in Ludhiana, India, a chance encounter helped shape his career.

“Dr. Norman Borlaug came to PAU in 2005 and he happened to visit my field experiment on bed planting wheat. I had a very inspiring conversation with him which made me decide to pursue a career in agricultural research and work for the farming community.”

Singh went on to earn a Ph.D. from Charles Sturt University in Australia through the John Allwright Fellowship funded by the Australian Center for International Agriculture Research (ACIAR). He started work for CIMMYT in 2013 as associate scientist based in New Delhi working with the Cereal Systems Initiative for South Asia (CSISA) project, which aims to improve food security and the livelihoods of more than 8 million farmers in South Asia by 2020.

Since 2014, Singh has led the CIMMYT participation in the  Agricultural Model Intercomparison and Improvement Project (AgMIP) as part of the Indo-Gangetic Basin team, conducting integrated assessments of the effects of climate change on global and regional food production and security, analyzing adaptation and mitigation measures.

Apart from collaborating with CIMMYT colleagues and other advanced research institutes from across the world to build weather and soil databases or working on simulation models, Singh enjoys interacting with farmers in their own fields and collecting data for crop simulation models to generate useable information for research and extension.

He also holds training sessions to aid in developing the capacity of CIMMYT’s national agricultural partners in system simulation modelling to create awareness of the proper use of simulation tools for research and extension.

“The most rewarding aspect of my work is to see my simulation results working in farmers’ fields,” Singh said. “There’s a proverb that says: ‘When a person is full they have a thousand wishes, but a hungry person has only one.’ There is no nobler task than that of being able to feed people. Some of us are not even aware of how many people are starving every day,” he said.

“It gives me great satisfaction to be a part of CIMMYT, an organization that works beyond political boundaries to safeguard future food security, improve livelihoods and carry on the legacy of Dr. Borlaug who fed billions.”

Crop and bio-economic modeling for an uncertain climate

workshop
Gideon Kruseman, CIMMYT ex-ante and foresight specialist presents household level bio-economic models at workshop. CIMMYT/Khondoker Mottaleb

Gideon Kruseman is CIMMYT’s ex-ante and foresight specialist.

The potential impact of climate change on agriculture and the complexity of possible adaptation responses require the application of new research methods and tools to develop adequate strategies. At a recent five-day training workshop titled “Crop and Bio-economic Modeling under Uncertain Climate,” scientists applied crop and bio-economic models to estimate biophysical and economic impacts of climate variability and change.

Crop system modeling is used to simulate yields for specific weather patterns, nutrient input levels and bio-economic household modeling involves using quantitative economic methodology to incorporate biological, chemical and/or physical processes to analyze the impact of technology development, policy interventions and such exogenous shocks as extreme weather events on the decision-making processes of smallholder farmers and related development indicators. Events influence results in two ways: the probability of occurrence will shape decision-making and actual occurrence will shape realized results.

During the training, which was organized and hosted by the International Maize and Wheat Improvement Center (CIMMYT), which took place in November in Kenya’s capital, Nairobi, scientists examined how technology development and policy or development interventions may influence farm household decisions on resource allocation and cropping patterns.

The training was beneficial due to its “holistic approach to solve smallholder agricultural production problem using decision support tools,” said Theodrose Sisay from the Ethiopian Institute of Agricultural Research.

Attendees learned in practical terms how shifting weather patterns will change farmer perception of the probability of occurrence of extreme events, which may influence subsequent cropping patterns and technology choices. Cropping system models shed light on the effects of different weather patterns on crop yields under varying management practices. Bio-economic household modeling then places those results in the context of smallholder livelihood strategies.

Bio-economic household model results demonstrated the conditions under which cropping patterns are likely to change as a result of resource constraints and household preferences. The analysis illustrated how cropping patterns may shift as a result of climate change:

bem-before-after-cc

Before climate change.                                          After climate change.

Figure: comparison of model results of climate change scenarios

The workshop was organized under the Global Futures & Strategic Foresight (GFSF) project and the “Flagship 1” component of the CGIAR Research Program on Policies, Institutions, and Markets (PIM), which in part explores global and regional foresight modeling tools.

Participants included representatives of the Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) and West and Central Africa Council for Agricultural Research and Development (CORAF), as well as researchers from agricultural research institutes and universities from Benin, Ethiopia, Kenya, Niger, Nigeria, Senegal and Uganda.

This was the third and last of a series of training workshops offered to same group of trainees since 2014. Not only did the 16 participants learn how to apply crop and bio-economic models allowing them to estimate biophysical and economic impacts of climate variability and change, but they also learned how to assess different adaptation options.

The tools they worked with included the Decision Support System for Agrotechnology Transfer (DSSAT), and a bio-economic household model using Gtree with the general algebraic modeling system (GAMS). The training involved plenary discussions, group work, and individual hands-on exercises.

The training program served as a refresher course on GAMS, said Janvier Egah, a socio-economist from Benin.

“Over time, I had forgotten everything,” he added. “With this training, I remembered the notions of the past course and learned new concepts such as integrating the costs of climate change in bio-economic models. These models interest me particularly and I want to write and submit proposals to apply them.”

The participants came with their own input data for the DSSAT cropping system model and learned how to calibrate the model. The participants developed climate change scenarios, ran simulations and interpreted the simulation outputs using graphical and statistical interfaces.

Workshop participants. Photo credit: CIMMYT
Workshop participants. Photo credit: CIMMYT

The participants, who have worked together in these workshops on three different occasions, indicated a strong willingness to continue collaborating after the conclusion of the project. They took steps to develop a concept note for a collaborative research grant with a major component related to the use of crop and bio-economic models.

The workshop had a stronger component related to the economic analysis of household decision-making than previous training sessions, and trainees used simulation models based on mathematical programming techniques.

At the conclusion of the workshop, participants expressed interest in pursuing further analysis of this type in the future as a complement to crop growth modelling.

Crop model gives scientists a window on future farming in the Eastern Gangetic Plains

In work to help farmers in South Asia tackle changing climates and markets through resilient and productive cropping systems, scientists are now using a leading and longstanding model, the Agricultural Production System Simulator (APSIM).

To foster better use of soil and water through conservation agriculture and other resource- conserving practices, the Sustainable and Resilient Farming System Intensification in the Eastern Gangetic Plains (SRFSI) project held an APSIM workshop for nine researchers from Bangladesh, India and Nepal at Bihar Agricultural University (BAU), Bihar, India during 27-29 January. The workshop was inaugurated by the Honourable Vice Chancellor, Dr. M.L. Choudhary, accompanied by Research Director Dr. Ravi Gopal Singh.

The Vice Chancellor of Bihar Agricultural University, Dr. M.L. Choudhary, opens the APSIM Exposure Workshop. L-R: Ms. Alison Laing (CSIRO), Dr. Don Gaydon (CSIRO), Mr. Ashraf Ali (CIMMYT-Bangladesh), Dr. Ravi Gopal Singh (BAU) and Dr. Choudhary. Photos: Alison Laing (CSIRO) and Ashraf Ali (CIMMYT).
The Vice Chancellor of Bihar Agricultural University, Dr. M.L. Choudhary, opens the APSIM Exposure Workshop. L-R: Ms. Alison Laing (CSIRO), Dr. Don Gaydon
(CSIRO), Mr. Ashraf Ali (CIMMYT-Bangladesh), Dr. Ravi Gopal Singh (BAU) and Dr. Choudhary. Photos: Alison Laing (CSIRO) and Ashraf Ali (CIMMYT).

“The aim was to introduce these colleagues to the model and help them explore its adaptation and use,” said Md. Ashraf Ali, CIMMYT scientist and manager of SRFSI, which was launched in 2014 and is funded by the Australian Centre for International Agricultural Research (ACIAR).

“Our research targets rice-based systems in eight districts across those three countries, where wheat is often a key part of the rotation and climate change is already constraining crop yields.”

– Mahesh Kumar Gathala

CIMMYT cropping systems agronomist

According to SRFSI lead scientist, Mahesh Kumar Gathala, a CIMMYT cropping systems agronomist based in Bangladesh, SERFI works in Bangladesh, SERFI works in northwestern Bangladesh, West Bengal and Bihar in India, and the eastern Terai region of Nepal. “Our research targets rice-based systems in eight districts across those three countries, where wheat is often a key part of the rotation and climate change is already constraining crop yields.”

Ved Prakash (L) and Swaraj Dutta (R) work on modeling exercises.

One response to climate change – conservation agriculture – involves a complex, knowledge-intensive suite of practices including reduced tillage, keeping crop residues on the soil surface and careful use of rotations. A model like APSIM can speed the design and adoption of approaches tailored to specific locations, Singh explained. “But to provide reliable results, the model has to be adapted for the soil, climate and other conditions of each area,” he said.

Led by Don Gaydon and Alison Laing from Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) and with practical assistance from Dr. Sanjay Kumar, BAU, and Ali, the course provided theory and practice on the APSIM user interface and how to manage data on soils, weather and soil dynamics such as residue decomposition and moisture levels. “We also looked at how to model direct-seeded rice and wheat crops, long-term crop rotations and cropping simulations under climate-change,” Ali said.

Once assembled, a project modelling team with members from Bangladesh, India, Nepal and CSIRO will identify relevant parameters, calibrate the model and test it for diverse locations. Ultimately they will analyze scenarios for diverse crop management options, both current and proposed.

“With APSIM we can virtually ‘extend’ SRFSI field trials into the future by twenty years or more, gaining insight on long-term system variability,” Gathala said. “We can also explore likely impacts of the region-wide outscaling of new management options from one farm or village, including effects of different options on sustainability or greenhouse gas emissions, which can be difficult or expensive to measure in the field.”

Ved Prakash (L) and Swaraj Dutta (R) work on modeling exercises.
Ved Prakash (L) and Swaraj Dutta (R) work on modeling exercises.