Tag: modeling

Gridded crop modeling to simulate impacts of climate change and adaptation benefits in ACASA

Written by Julian Bañuelos-Uribe on May 14, 2024. Posted in Blogs.

Global temperatures are projected to warm between 1.5-2 degrees Celsius by the year 2050, and 2-4 degrees Celsius by 2100. This is likely to change precipitation patterns, which will impact crop yields, water availability, food security, and agricultural resilience.

To prepare for these challenges, Atlas of Climate Adaptation in South Asian Agriculture (ACASA) uses process-based simulation models that can predict crop growth, development, and yield in order to understand the response of crops to climate change. Models such as Decision Support System for Agrotechnology Transfer (DSSAT), InfoCrop, and Agricultural Production Systems Simulator (APSIM) facilitate the field scale study of the biophysical and biochemical processes of crops under various environmental conditions, revealing how they are affected by changing weather patterns.

The ACASA team, along with experts from Columbia University and the University of Florida, met for a three-day workshop in January 2024 to boost the work on spatial crop modeling. The aim was to design a modeling protocol through a hands-on demonstration on high-performance computers. When scientifically executed, gridded spatial crop modeling–even though complex and data-intensive–can be a great way to frame adaptation and mitigation strategies for improving food security, which is one of ACASA’s goals.

ACASA’s Spatial Crop Modelling Group meets in Colombo, Sri Lanka, January 2024. (Photo: CIMMYT)

Decisions on data

The group decided to use DSSAT, APSIM, and InfoCrop for simulating the impact of climatic risks on crops such as rice, wheat, maize, sorghum, millet, pigeon pea, chickpea, groundnut, soybean, mustard, potato, cotton, and more. They chose harmonized protocols across all three models with standard inputs, such as conducting simulations at 0.05 degrees. The model input data about weather, soil, crop varietal coefficients, and crop management are being collected and processed for model input formats at 5 kilometer (km) spatial resolution.

A Python version called DSSAT-Pythia is now available to accelerate spatial and gridded applications. The programming for implementing InfoCrop on the Pythia platform is in progress. InfoCrop has been proven in India for past yield estimations, climate change spatial impact, and adaptation assessments for 12 crops.

For other crucial modeling components, a work plan was created including developing regional crop masks, crop zones based on mega-commodity environments as defined by CGIAR, production systems, crop calendars, and irrigated areas by crop. Genetic coefficients will then be calculated from measured past values and recent benchmark data of varietal units.

With this information, several adaptation options will be simulated, including changes in planting dates, stress-tolerant varieties, irrigation, and nitrogen fertilizer (quantity, methods, and technology), residue/mulching, and conservation tillage. The team will evaluate impact and adaptation benefits on yields, water, and nitrogen-use efficiency based on the reported percentage change from the baseline data.

As the project progresses, this work will make strides towards realizing food security for the planet and increasing the resilience of smallholder farming practices.

Blog written by Anooja Thomas, University of Florida; Apurbo K Chaki, BARI, Bangladesh; Gerrit Hoogenboom, University of Florida; S Naresh Kumar, ICAR-IARI, India

Building towards a climate-smart agriculture future through harnessing crop modeling

Written by Sarah McLaughlin on February 24, 2023. Posted in News.

Participants of the crop modeling simulation workshop in Harare, Zimbabwe. (Photo: Tawanda Hove/CIMMYT)

Anticipating appropriate and timely responses to climate variability and change from an agricultural perspective requires forecasting and predictive capabilities. In Africa, climate-related risks and hazards continue to threaten food and nutrition security.

Crop simulation models are tools developed to assist farmers, agronomists and agro-meteorologists with insights on impacts to possible management decisions. Such tools are enablers for taking an appropriate course of action where complexity exists relating to both crop and livestock production. For example, a new variety can be introduced to Zimbabwe, but its performance will differ depending on the agroecological zones of the country and the respective treatments a farmer may apply. Applying modeling tools to assess its performance can predict yield differences and facilitate the generation of recommendations for which region is most suited to the variety, water use efficiency, and crop combinations.

Earlier this month, the International Maize and Wheat Improvement Center (CIMMYT) hosted a crop modeling simulation workshop with delegates from various African countries in Harare, Zimbabwe.

“The CGIAR Initiatives of Excellence in Agronomy (EiA) and Sustainable Intensification of Mixed Farming Systems (SI-MFS) have recognized the need to enhance modeling capacity in Africa to allow African scientists to lead in solving challenges within agricultural systems,” said CIMMYT crop scientist and coordinator of the workshop, Vimbayi Grace Petrova Chimonyo.

The workshop was facilitated by renowned global crop modeling experts to provide critical coaching support to upcoming modelers. These experts included Sue Walker, a professor at the University of the Free State, Tafadzwa Mabhaudhi, a professor at the International Water Management Institute (IWMI), KPC Rao, a lead scientist at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Dirk Raes (KU Leuven), Diego Peqeuno (CIMMYT) and Siyabusa Mukuhlani from the International Institute of Tropical Agriculture (IITA).

Crop models are scientific presentations of statistical knowledge about how a crop will grow in interaction with its environment. They use mathematical equations representing processes within a predefined plant system and the interactions between crops and the environment. The discipline is based on the premise that agricultural system environments are complex and not homogenous. Crop models enable decision-makers to make data-driven decisions by simulating possible outcomes to changes in a system and the configuration of production systems.

“It is quite apparent that modeling skills are scarce on the African continent. This workshop is a step toward consolidating existing capacities on the continent. If we are going to be able to close the already existing food deficit gap on the continent and meet the food requirements needed by 2050, with an estimated global population of nine billion, then we need to take modeling seriously,” said Chimonyo in her opening address at the workshop.

Due to the lack of crop modeling expertise in African states, there is a gap in capacity to build relevant crop advisory tools for farmers at a local level. This leads to poor policy formulation as decisions are based on a high degree of generalizations.

“In this modern era, we need advisories that are context specific. For example, just because a maize variety achieved a certain yield in one context doesn’t mean the same variety will achieve the same yields even if the rainfall patterns are the same. Other factors come into play, such as the soil type, temperature and other related aspects affecting the yield. Crop modeling affords advisory managers some specifications necessary to achieve high yields in different environments,” said Walker.

Vimbayi Chimonyo from CIMMYT making opening remarks at the workshop. (Photo: Tawanda Hove/CIMMYT)

Speakers at the workshop focused on three models, APSIM, AquaCrop and DSSAT, and participants had the opportunity to take part in activities and ask questions face-to-face. The workshop also covered key modeling aspects such as the minimum data requirements needed to run a model, calibration and validation of models, confidence testing of results, the science involved in simulating phenological development and growth processes, water and nitrogen cycles, and the use of multi-modeling approaches.

The workshop was particularly useful for young scientists, according to Rao, allowing more experienced modelers to share their expertise. “With such an interactive platform, experienced modelers like me can demonstrate multi-modeling approaches.”

Rao presented on two main approaches. The first involved the application of different simulation models to simulate one component of a system such as crops. The second simulated the complete system by integrating various models, such as crops, livestock, and economic models, providing an opportunity to understand the synergies and trade-offs between different components of the whole farm.

Participants at the workshop expressed their satisfaction with the training provided and left with practical knowledge that they could apply in their work both in the field and in the lab.

“When I first arrived, I knew very little about modeling, but as the workshop progressed, my confidence in applying models increased. I intend to immediately apply this knowledge for the forthcoming season such that we can start making impactful contributions to the country’s food and nutrition security status,” said Birhan Abdulkadir Indris, a research officer at CIMMYT.

“I am leaving this workshop with the confidence that I will advise farmers in my circle of influence with services tailored to their needs. I have learned that crop modeling can be used for many purposes and that different models address different issues,” said Connie Madembo, a research technician at CIMMYT. “I intend to teach other fellow PhD students at the University of Zimbabwe the same things I have learnt here. As a country, we need to be at the forefront of using these models, considering Zimbabwe’s high weather variability.”

As a way forward, the trained scientists were encouraged to apply the modeling skills they had gained to address short-term problems such as yield gaps and water use efficiency and long-term challenges such as the local impacts of climate change.

“While more capacity training is required, starting somewhere is better than never starting,” said Mabhaudi.

Adapting growing seasons to climate change can boost yields of world’s staple crops

Written by Alison Doody on January 24, 2023. Posted in Publications.

Rising global temperatures due to climate change are changing the growth cycles of crops worldwide. Recent records from Europe show that wild and cultivated plants are growing earlier and faster due to increased temperatures.

Farmers also influence the timing of crops and tend to grow their crops when weather conditions are more favorable. With these periods shifting due to climate change, sowing calendars are changing over time.

Over thousands of years of domesticating and then breeding crops, humans have also managed to artificially change how crop varieties respond to both temperature and day length, and in turn have been able to expand the area where crop species can be grown. Farmers can now choose varieties that mature at different rates and adapt them to their environment.

Including farmers’ decisions on when to grow crops and which varieties to cultivate are vital ingredients for understanding how climate change is impacting staple crops around the world and how adaptation might offset the negative effects.

In a ground-breaking study, a team of researchers from the Potsdam Institute for Climate Impact Research (PIK), the Technical University of Munich and the International Maize and Wheat Improvement Center (CIMMYT) investigated how farmers’ management decisions affect estimates of future global crop yields under climate change.

“For long time, the parametrization of global crop models regarding crop timing and phenology has been a challenge,” said Sara Minoli, first author of the study. “The publication of global calendars of sowing and harvest have allowed advancements in global-scale crop model and more accurate yield simulations, yet there is a knowledge gap on how crop calendars could evolve under climate change. If we want to study the future of agricultural production, we need models that can simulate not only crop growth, but also farmers’ management decisions.”

Using computer simulations and process-based models, the team projected the sowing and maturity calendars for five staple crops, maize, wheat, rice, sorghum and soybean, adapted to a historical climate period (1986–2005) and two future periods (2060–2079 and 2080–2099). The team then compared the crop growing periods and their corresponding yields under three scenarios: no adaptation, where farmers continue with historical sowing dates and varieties; timely adaptation, where farmers adapt sowing dates and varieties in response to changing climate; and delayed adaptation, where farmers delay changing their sowing dates and varieties by 20 years.

The results of the study, published last year in Nature Communications, revealed that sowing dates driven by temperature will have larger shifts than those driven by precipitation. The researchers found that adaptation could increase crop yields by 12 percent, compared to non-adaptation, with maize and rice showing the highest potential for increased crop yields at 17 percent. This in turn would reduce the negative impacts of climate change and increase the fertilization effect of increased levels of carbon dioxide (CO₂) in the atmosphere.

They also found that later-maturing crop varieties will be needed in the future, especially at higher latitudes.

“Our findings indicate that there is space for maintaining and increasing crop productivity, even under the threat of climate change. Unfortunately, shifting sowing dates – a very low-cost measure – is not sufficient, and needs to be complemented by the adaptation of the entire cropping cycle through the use of different cultivars,” said Minoli.

Another important aspect of this study, according to Anton Urfels, CIMMYT systems agronomist and co-author of the study, is that it bridges the GxMxE (Gene-Management-Environment) spectrum by using crop simulations as an interdisciplinary tool to evaluate complex interactions across scientific domains.

“Although the modeled crops do not represent real cultivars, the results provide information for breeders regarding crop growth durations (i.e. the need for longer duration varieties) needed in the future as well as agronomic information regarding planting and harvesting times across key global climatic regimes. More such interdisciplinary studies will be needed to address the complex challenges we face for transitioning our food systems to more sustainable and resilient ones,” said Urfels.

Read the study: Global crop yields can be lifted by timely adaptation of growing periods to climate change

Cover photo: Work underway at the International Maize and Wheat Improvement Center in Zimbabwe (CIMMYT), is seeking to ensure the widespread hunger in the country caused by the 2015/6 drought is not repeated, by breeding a heat and drought tolerant maize variety that can still grow in extreme temperatures. CIMMYT maize breeders used climate models from the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) to inform breeding decisions. (Photo: L. Sharma/Marchmont Communications)

Scientist urges upgrades to monitor groundwater use for agriculture in low-income countries

Written by Mike Listman on September 27, 2022. Posted in News.

Data collector reading data from offline groundwater level logger – one of the three tested monitoring technologies. (Credit: Subash Adhikari/CIMMYT)

Based on a pilot study regarding the feasibility and cost effectiveness of several groundwater monitoring approaches for agriculture in Nepal’s Terai region, a water and food security specialist who led the research has recommended the use of phone-based systems.

Speaking to diverse experts at the recent World Water Week 2022 in Stockholm, Sweden, Anton Urfels, a systems agronomist at the International Maize and Wheat Improvement Center (CIMMYT), said that manual monitoring with phone-based data uploading is relatively low-cost and effective and could be scaled up across the Terai.

“One alternate monitoring approach studied — online data uploading — has substantially lower staff time requirements and technology costs and higher temporal resolution than phone-based monitoring, but does not provide real-time data and entails high technical skills, capital costs, and risks of theft and damage,” said Urfels in his presentation, ‘Upgrading Groundwater Monitoring Networks in Low-Income Countries’.

Urfels and partners also developed a prototype of an open-source groundwater monitoring dashboard to engage stakeholders, help translate raw data into actionable information, and detect water depletion trends.

Water has become a key part of food research and innovation, critical for sustainable and ecological intensification in agriculture, according to the scientist.

“Collecting groundwater data is difficult and the technology for monitoring is unreliable, which impairs effective modeling, decision-making, and learning,” Urfels explained. “Like other countries in the region, Nepal is increasing its agricultural groundwater consumption, particularly through private investment in irrigation wells and pumps that open irrigation to more farmers. This and climate change have altered groundwater recharge rates and availability, but national data on these trends are incomplete.”

An extensive lowland region bordering India and comprising one-fifth of the nation’s territory, the Terai is Nepal’s breadbasket.

Held yearly since 1991, World Water Week attracts a diverse mix of participants from many professions to develop solutions for water-related challenges including poverty, the climate crisis, and biodiversity loss. The 2022 theme was “Seeing the Unseen: The Value of Water”.

“I’d recommend more pilot studies on phone-based groundwater monitoring for other areas of Nepal, such as the Mid-hill districts,” Urfels said. “We also need to fine-tune and expand the system dashboard and build cross-sectoral coordination to recognize and take into account groundwater’s actual economic value.”

Urfels said the Nepal Ministry of Energy, Water Resources and Irrigation has requested the nationwide scale-out of a digital monitoring system, and CIMMYT and Nepal experts will support this, as well as improving the system, which would be freely available for use and development by researchers and agencies outside of Nepal.

The research described was carried out under the Cereal Systems Initiative in South Asia (CSISA), which is funded by USAID and the Bill & Melinda Gates Foundation, and under the CGIAR integrated research initiative, Transforming Agrifood Systems in South Asia (TAFSSA).

Researchers take part in Wheat Blast screening and surveillance course in Bangladesh. (Photo: CIMMYT/Tim Krupnik)

Weather data and crop disease simulations can power predictions of wheat blast outbreaks, new study shows

Written by Mike Listman on September 26, 2022. Posted in Publications.

Cutting-edge models for crops and crop diseases, boosted by high-resolution climate datasets, could propel the development of early warning systems for wheat blast in Asia, helping to safeguard farmers’ grain supplies and livelihoods from this deadly and mysterious crop disease, according to a recent study by scientists at the International Maize and Wheat Improvement Center (CIMMYT).

Originally from the Americas, wheat blast shocked farmers and experts in 2016 by striking 15,000 hectares of Bangladesh wheat fields, laying waste to a third of the crops. The complex interactions of wheat and the fungus, Magnaporthe oryzae pathotype Triticum (MoT), which causes blast, are not fully understood. Few current wheat varieties carry genetic resistance to it and fungicides only partly control it. Warm temperatures and high humidity favor MoT spore production and spores can fly far on winds and high-altitude currents.

Mean potential wheat blast disease infections (NPI) across Asia, based on disease and crop infection model simulations using air temperature and humidity data from 1980-2019. Black dots represent wheat growing areas with presumably unsuitable climates for wheat blast. The x and y axes indicate longitude and latitude.

“Using a wheat blast infection model with data for Asia air temperatures and humidity during 1980-2019, we found high potential for blast on wheat crops in Bangladesh, Myanmar, and areas of India, whereas the cooler and drier weather in countries such as Afghanistan and Pakistan appear to render their wheat crops as unlikely for MoT establishment,” said Carlo Montes, a CIMMYT agricultural climatologist and first author of the paper, published in the International Journal of Biometeorology. “Our findings and approach are directly relevant for work to strengthen monitoring and forecasting tools for wheat blast and other crop diseases, as well as building farmers’ and agronomists’ disease control capacity.”

Montes emphasized the urgency of those efforts, noting that some 13 million hectares in South Asia are sown to wheat in rotation with rice and nearly all the region’s wheat varieties are susceptible to wheat blast.

Read the full study: Variable climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum) in Asia: Results from a continental‑scale modeling approach

Cover photo: Researchers take part in a wheat blast screening and surveillance course in Bangladesh. (Photo: CIMMYT/Tim Krupnik)

Crop Modeling community of practice

Written by Rodrigo Ordóñez on October 30, 2020. Posted in Videos.

The Community of Practice on Crop Modeling is part of the CGIAR Platform for Big Data in Agriculture and encompasses a wide range of quantitative applications, based around the broad concept of parametrizing interactions within and among the main drivers of cropping systems. These are namely: Genotype, Environment, Management and Socioeconomic factors (GEMS) to provide information and tools for decision support. The Community of Practice was formed in 2017 and is led by Wheat Physiologist Matthew Reynolds at the International Maize and Wheat Improvement Center (CIMMYT) in Texcoco, Mexico.

Crop modeling has already contributed to a better understanding of crop performance and yield gaps; predictions of potential pest and disease epidemics; more efficient irrigation and fertilization systems, and optimized planting dates. These outputs help decision makers look ahead and prepare their research and extension systems to fight climate change where it is most needed. However, there is a significant opportunity — and need — to improve the global coordination of crop modeling efforts in agricultural research. This will, in turn, greatly improve the world’s ability to develop more adaptive, resilient crops and cropping systems.

Our Community of Practice aims to promote a better-coordinated and more standardized approach to crop modeling in agricultural research. With over 900 members involving CGIAR centers and a wide range of international partners, the Crop Modeling Community of Practice is already facilitating and sharing knowledge, resources, “model-ready” data, FAIR (Findable, Accessible, Interoperable, Reusable) data principles, and other useful information; while promoting capacity building and collaboration within the CGIAR and its community.

Get more information about the Crop Modeling Community of Practice on the Big Data website.

Join the Crop Modeling mailing list to get information about publications, webinars, new tools, updates and collaboration opportunities.

Connect to our LinkedIn group: Crop Modeling CoP.

New publications: Role of Modelling in International Crop Research

Written by Emma Orchardson on January 18, 2019. Posted in Publications.

“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:

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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

Written by on May 23, 2018. Posted in News.

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.

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

Written by on August 15, 2017. Posted in Publications.

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

Written by on May 25, 2017. Posted in Features.

Breaking 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

Written by on December 19, 2016. Posted in Blogs.

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:

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

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

Written by on February 23, 2015. Posted in News.

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.”