Tag: geospatial modeling

New CSISA Infographic highlights the impact of the CIMMYT’s Soil Intelligence System (SIS)

Written by Nima Chodon on November 9, 2021. Posted in News.

In agriculture, good soil management is a pillar of productive systems that can sustainably produce sufficient and healthy food for the world’s growing population.

Soil properties, however, vary widely across geography. To understand the productive capacity of our soils, we need high-quality data. Soil Intelligence System (SIS) is an initiative to develop comprehensive soil information at scale under the Cereal Systems Initiative for South Asia (CSISA) project in India. SIS is led by the International Maize and Wheat Improvement Centre (CIMMYT) in collaboration with ISRIC – World Soil Information, International Food Policy Research Institute (IFPRI), and numerous local partners on the ground.

Funded by the Gates Foundation, the initiative launched in 2019 helps rationalize the costs of generating high-quality soils data while building accessible geo-spatial information systems based on advanced geo-statistics. SIS is currently operational in the States of Andhra Pradesh, Bihar and Odisha where the project partners collaborate with state government and state agricultural universities help produce robust soil health information.

Farmers are the primary beneficiaries of this initiative, as they get reliable soil health management recommendations to increase yields and profits sustainably while state partners, extension and agricultural development institutions and private sector benefit primarily by expanding their understanding for agricultural interventions.

Modern Soil Intelligence System Impact

CIMMYT’s SIS Project lead Balwinder Singh said, “The Soil Intelligence Systems initiative under CSISA is an important step towards the sustainable intensification of agriculture in South Asia. SIS has helped create comprehensive soil information – digital soil maps – for the states of Andhra Pradesh, Bihar and Odisha. The data generated through SIS is helping stakeholders to make precise agronomy decisions at scale that are sustainable.”

Since its launch in December 2019, a wider network and multi-institutional alliances have been built for soil health management and the application of big data in addressing agricultural challenges. In the three states the infrastructure and capacity of partners have been strengthened to leverage soil information for decision-making in agriculture by devising new soil health management recommendations. For example, in the state of Andhra Pradesh, based on SIS data and outreach, State Fertilizer and Micronutrient Policy (SFMP) recommendations were created. Similarly, soil health management zones have been established to strengthen the fertilizer distribution markets enabling farmers with access and informed choices.

“Soil Intelligence System delivers interoperable information services that are readily usable by emerging digital agricultural decision support systems in India”, noted Kempen Senior Soil Scientist at ISRIC.

The three-part infographic highlights the impact of SIS initiative in the select three States and emphasizes the importance of SIS in other parts of the country as well.

Breaking Ground: Jordan Chamberlin avidly explores the changing landscapes of Africa

Written by Jérôme Bossuet on October 1, 2020. Posted in Features.

Sub-Saharan Africa is undergoing important transformations, including climate change, population growth, urbanization and migration flows, and growth in digital technologies. What can we say about the likely development trajectories that African rural economies are on, and the implications for poor farming households? These are central questions for Jordan Chamberlin, an economist at the International Maize and Wheat Improvement Center (CIMMYT) in Kenya.

Chamberlin’s desk is covered with screens teeming with numbers, complex mathematical equations, lines of code and aerial views of African landscapes. He combines traditional microeconomic analysis with geospatial modelling skills to study some of the ways in which rural transformations are occurring. In this era of big data, he examines the wealth of spatial and socioeconomic datasets to explore the relationships between drivers of change and smallholder welfare, sometimes revealing surprising insights on how rural communities in Africa are evolving.

“Are commercial farms good or bad for neighboring smallholder farmers? Which households can benefit from the rapidly evolving rural land markets in Africa? What drives migration between rural areas? These are some examples of the complex but increasingly important questions that inform how we understand the evolution of agri-food systems in developing countries,” Chamberlin explains. “Fortunately, we also increasingly have access to new data that helps us explore these issues.”

In addition to household survey datasets — the bread and butter of applied social scientists — today’s researchers are also able to draw on an ever-expanding set of geospatial data that helps us to better contextualize the decisions smallholder farmers make.

He cites current work, which seeks to understand input adoption behaviors through better measurement of the biophysical and marketing contexts in which small farms operate. “Evidence suggests that low use rates of inorganic fertilizer by smallholders is due in part to poor expected returns on such investments,” he explains, “which are the result of site-specific agronomic responses, rainfall uncertainty, variation in input-output price ratios, and other factors.”

We are increasingly able to control for such factors explicitly: one of Chamberlin’s recent papers shows the importance of soil organic carbon for location-specific economic returns to fertilizer investments in Tanzania. “After all, farmers do not care about yields for yields’ sake — they make agronomic investments on the basis of how those investments affect their economic welfare.”

Better data and models may help to explain why farmers sometimes do not adopt technologies that we generally think of as profitable. A related strand of his research seeks to better model the spatial distribution of rural market prices.

Jordan Chamberlin (left) talks to a farmer in Ethiopia’s Tigray region in 2019, while conducting research on youth outmigration from rural areas. (Photo: Jordan Chamberlin)

A spatial economist’s journey on Earth

Ever since his experience as a Peace Corps volunteer in Paraguay, where he worked as a beekeeping specialist, Chamberlin knew he wanted to spend his professional life working with smallholder farmers. He wanted to better understand how rural development takes place, and how policies and investments can help rural households to improve their welfare.

In pursuit of these interests, his academic journey took him from anthropology to quantitative geography, before leading him to agricultural economics. “While my fundamental interest in rural development has not changed, the analytical tools I have preferred have evolved over the years, and my training reflects that evolution,” he says.

Along with his research interests, he has always been passionate about working with institutions within the countries where his research has focused. While working with the International Food Policy Research Institute (IFPRI) in Ethiopia, he helped establish a policy-oriented GIS lab at the Ethiopian Development Research Institute (EDRI). Years later, as part of his work with Michigan State University, he served as director of capacity building at the Indaba Agricultural Policy Research Institute (IAPRI), a not-for-profit Zambian research organization. He continues to serve as an external advisor on PhD committees, and considers mentorship a key part of his professional commitments.

He joined CIMMYT at the Ethiopia office in 2015 as spatial economist, part of the foresight and ex ante group of the Socioeconomics program.

As part of his research portfolio, he explores the role of new technologies, data sources and extension methods in the scaling of production technologies. Under the Taking Maize Agronomy to Scale in Africa (TAMASA) project, one area he has been working on is how we may better design location-specific agronomic advisory tools. Working with the Nutrient Expert tool, developed by the African Plant Nutrition Institute (APNI), he and his research team have conducted randomized control trials in Ethiopia and Nigeria to evaluate the impacts of such decision-support tools on farmer investments and productivity outcomes. They found that such tools appear to contribute to productivity gains, although tool design matters — for example, Nigerian farmers were more likely to take up site-specific agronomic recommendations when such information was accompanied by information about uncertainty of financial returns.

Jordan Chamberlin (center) talks to colleagues during a staff gathering in Nairobi. (Photo. Joshua Masinde/CIMMYT)

Creative rethinking

While Chamberlin’s research portfolio is diverse, one commonality is the drive to use new data and tools to better guide how development resources are allocated.

“Given the scarcity of resources available to governments and their partners, it is important to have sound empirical foundations for the allocation of these resources. Within CIMMYT, I see my role as part of a multidisciplinary team whose goal is to generate such empirical guidance,” he says.

This research also contributes to better design of agricultural development policies.

“Even though many of the research topics that my team addresses are not traditional areas of emphasis within CIMMYT’s socioeconomic work, I hope that we are demonstrating the value of broad thinking about development questions, which are of fundamental importance to one of our core constituencies: the small farmers of the region’s maize and wheat-based farming systems.”

New Publications: Cropping pattern zonation of Pakistan

Written by Mike Listman on May 20, 2020. Posted in Publications. 1 Comment on New Publications: Cropping pattern zonation of Pakistan

The tremendous diversity of crops in Pakistan has been documented in a new publication that will foster more effective and targeted policies for national agriculture.

Using official records and geospatial modeling to describe the location, extent, and management of 25 major and minor crops grown in 144 districts of Pakistan, the publication “Cropping Pattern Zonation of Pakistan” offers an invaluable tool for resource planning and policymaking to address opportunities, challenges and risks for farm productivity and profitability, according to Muhammad Imtiaz, crop scientist and country representative in Pakistan for the International Maize and Wheat Improvement Center (CIMMYT).

“With rising temperatures, more erratic rainfall and frequent weather extremes, cropping pattern decisions are of the utmost importance for risk mitigation and adaptation,” said Imtiaz, a co-author of the new publication.

Featuring full-color maps for Pakistan’s two main agricultural seasons, based on area sown to individual crops, the publication was put together by CIMMYT and the Climate, Energy and Water Research Institute (CEWRI) of the Pakistan Agricultural Research Council (PARC), with technical and financial support from the Agricultural Innovation Program (AIP) for Pakistan, which is funded by the U.S. Agency for International Development (USAID).

Pakistan’s main crops–wheat, rice, cotton and sugarcane—account for nearly three-quarters of national crop production. Various food and non-food crops are grown in “Rabi,” the dry winter season, October-March, and “Kharif,” the summer season characterized by high temperatures and monsoon rains.

Typically, more than one crop is grown in succession on a single field each year; however, despite its intensity, farming in Pakistan is largely traditional or subsistence agriculture dominated by the food grains, according to Ms. Rozina Naz, Principal Scientific Officer, CEWRI-PARC.

“Farmers face increasing aridity and unpredictable weather conditions and energy shortage challenges that impact on their decisions regarding the type and extent of crops to grow,” said the scientist, who is involved in executing the whole study. “Crop pattern zoning is a pre-requisite for the best use of land, water and capital resources.”

The study used 5 years (2013-14 to 2017-18) of data from the Department of Agricultural Statistics, Economics Wing, Ministry of National Food Security and Research, Islamabad. “We greatly appreciate the contributions of scientists and technical experts of Crop Science Institute (CSI) and CIMMYT,” Imtiaz added.

View or download the publication:
Cropping Pattern Zonation of Pakistan. Climate, Energy and Water Research Institute, National Agricultural Research Centre, Pakistan Agricultural Research Council, and the International Maize and Wheat Improvement Center. 2020. CDMX: CEWRI, PARC, and CIMMYT.

See more recent publications from CIMMYT researchers:

1. Plant community strategies responses to recent eruptions of Popocatépetl volcano, Mexico. 2019. Barba‐Escoto, L., Ponce-Mendoza, A., García-Romero, A., Calvillo-Medina, R.P. In: Journal of Vegetation Science v. 30, no. 2, pag. 375-385.

2. New QTL for resistance to Puccinia polysora Underw in maize. 2019. Ce Deng, Huimin Li, Zhimin Li, Zhiqiang Tian, Jiafa Chen, Gengshen Chen, Zhang, X, Junqiang Ding, Yuxiao Chang In: Journal of Applied Genetics v. 60, no. 2, pag. 147-150.

3. Hybrid wheat: past, present and future. 2019. Pushpendra Kumar Gupta, Balyan, H.S., Vijay Gahlaut, Pal, B., Basnet, B.R., Joshi, A.K. In: Theoretical and Applied Genetics v. 132, no. 9, pag. 2463-2483.

4. Influence of tillage, fertiliser regime and weeding frequency on germinable weed seed bank in a subhumid environment in Zimbabwe. 2019. Mashavakure, N., Mashingaidze, A.B., Musundire, R., Gandiwa, E., Thierfelder, C., Muposhi, V.K., Svotwa, E.In: South African Journal of Plant and Soil v. 36, no. 5, pag. 319-327.

5. Identification and mapping of two adult plant leaf rust resistance genes in durum. 2019. Caixia Lan, Zhikang Li, Herrera-Foessel, S., Huerta-Espino, J., Basnet, B.R., In: Molecular Breeding v. 39, no. 8, art. 118.

6. Genetic mapping reveals large-effect QTL for anther extrusion in CIMMYT spring wheat. 2019. Muqaddasi, Q.H., Reif, J.C., Roder, M.S., Basnet, B.R., Dreisigacker, S. In: Agronomy v. 9 no. 7, art. 407.

7. Growth analysis of brachiariagrasses and ‘tifton 85’ bermudagrass as affected by harvest interval. 2019. Silva, V. J. da., Faria, A.F.G., Pequeno, D.N.L., Silva, L.S., Sollenberger, L.E., Pedreira, C. G. S. In: Crop Science v. 59, no. 4, pag. 1808-1814.

8. Simultaneous biofortification of wheat with zinc, iodine, selenium, and iron through foliar treatment of a micronutrient cocktail in six countries. 2019. Chunqin Zou, Yunfei Du, Rashid, A., Ram, H., Savasli, E., Pieterse, P.J., Ortiz-Monasterio, I., Yazici, A., Kaur, C., Mahmood, K., Singh, S., Le Roux, M.R., Kuang, W., Onder, O., Kalayci, M., Cakmak, I. In: Journal of Agricultural and Food Chemistry v. 67, no. 29, pag. 8096-8106.

9. Economic impact of maize stem borer (Chilo partellus) attack on livelihood of maize farmers in Pakistan. 2019. Ali, A., Issa, A.B. In: Asian Journal of Agriculture and Biology v. 7, no. 2, pag. 311-319.

10. How much does climate change add to the challenge of feeding the planet this century?. 2019. Aggarwal, P.K., Vyas, S., Thornton, P.K., Campbell, B.M. In: Environmental Research Letters v. 14 no. 4, art. 043001.

11. A breeding strategy targeting the secondary gene pool of bread wheat: introgression from a synthetic hexaploid wheat. 2019. Ming Hao, Lianquan Zhang, Laibin Zhao, Shoufen Dai, Aili Li, Wuyun Yang, Die Xie, Qingcheng Li, Shunzong Ning, Zehong Yan, Bihua Wu, Xiujin Lan, Zhongwei Yuan, Lin Huang, Jirui Wang, Ke Zheng, Wenshuai Chen, Ma Yu, Xuejiao Chen, Mengping Chen, Yuming Wei, Huaigang Zhang, Kishii, M, Hawkesford, M.J, Long Mao, Youliang Zheng, Dengcai Liu In: Theoretical and Applied Genetics v. 132, no. 8, pag. 2285-2294.

12. Sexual reproduction of Zymoseptoria tritici on durum wheat in Tunisia revealed by presence of airborne inoculum, fruiting bodies and high levels of genetic diversity. 2019. Hassine, M., Siah, A., Hellin, P., Cadalen, T., Halama, P., Hilbert, J.L., Hamada, W., Baraket, M., Yahyaoui, A.H., Legreve, A., Duvivier, M. In: Fungal Biology v. 123, no. 10, pag. 763-772.

13. Influence of variety and nitrogen fertilizer on productivity and trait association of malting barley. 2019. Kassie, M., Fantaye, K. T. In: Journal of Plant Nutrition v. 42, no. 10, pag. 1254-1267.

14. A robust Bayesian genome-based median regression model. 2019. Montesinos-Lopez, A., Montesinos-Lopez, O.A., Villa-Diharce, E.R., Gianola, D., Crossa, J. In: Theoretical and Applied Genetics v. 132, no. 5, pag. 1587-1606.

15. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. 2019. Jin Sun, Poland, J.A., Mondal, S., Crossa, J., Juliana, P., Singh, R.P., Rutkoski, J., Jannink, J.L., Crespo-Herrera, L.A., Velu, G., Huerta-Espino, J., Sorrells, M.E. In: Theoretical and Applied Genetics v. 132, no. 6, pag. 1705-1720.

16. Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits. 2019. Varshney, R.K., Thudi, M., Roorkiwal, M., Weiming He, Upadhyaya, H., Wei Yang, Bajaj, P., Cubry, P., Abhishek Rathore, Jianbo Jian, Doddamani, D., Khan, A.W., Vanika Garg, Annapurna Chitikineni, Dawen Xu, Pooran M. Gaur, Singh, N.P., Chaturvedi, S.K., Nadigatla, G.V.P.R., Krishnamurthy, L., Dixit, G.P., Fikre, A., Kimurto, P.K., Sreeman, S.M., Chellapilla Bharadwaj, Shailesh Tripathi, Jun Wang, Suk-Ha Lee, Edwards, D., Kavi Kishor Bilhan Polavarapu, Penmetsa, R.V., Crossa, J., Nguyen, H.T., Siddique, K.H.M., Colmer, T.D., Sutton, T., Von Wettberg, E., Vigouroux, Y., Xun Xu, Xin Liu In: Nature Genetics v. 51, pag. 857-864.

17. Farm typology analysis and technology assessment: an application in an arid region of South Asia. 2019. Shalander Kumar, Craufurd, P., Amare Haileslassie, Ramilan, T., Abhishek Rathore, Whitbread, A. In: Land Use Policy v. 88, art. 104149.

18. MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens. 2019. Radhakrishnan, G.V., Cook, N.M., Bueno-Sancho, V., Lewis, C.M., Persoons, A., Debebe, A., Heaton, M., Davey, P.E., Abeyo Bekele Geleta, Alemayehu, Y., Badebo, A., Barnett, M., Bryant, R., Chatelain, J., Xianming Chen, Suomeng Dong, Henriksson, T., Holdgate, S., Justesen, A.F., Kalous, J., Zhensheng Kang, Laczny, S., Legoff, J.P., Lesch, D., Richards, T., Randhawa, H. S., Thach, T., Meinan Wang, Hovmoller, M.S., Hodson, D.P., Saunders, D.G.O. In: BMC Biology v. 17, no. 1, art. 65.

19. Genome-wide association study for multiple biotic stress resistance in synthetic hexaploid wheat. 2019. Bhatta, M.R., Morgounov, A.I., Belamkar, V., Wegulo, S.N., Dababat, A.A., Erginbas-Orakci, G., Moustapha El Bouhssini, Gautam, P., Poland, J.A., Akci, N., Demir, L., Wanyera, R., Baenziger, P.S. In: International Journal of Molecular Sciences v. 20, no. 15, art. 3667.

20. Genetic diversity and population structure analysis of synthetic and bread wheat accessions in Western Siberia. 2019. Bhatta, M.R., Shamanin, V., Shepelev, S.S., Baenziger, P.S., Pozherukova, V.E., Pototskaya, I.V., Morgounov, A.I. In: Journal of Applied Genetics v. 60, no. 3-4, pag. 283-289.

21. Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS. 2019. Jing Li, Gou-Bo Chen, Rasheed, A., Delin Li, Sonder, K., Zavala Espinosa, C., Jiankang Wang, Costich, D.E., Schnable, P.S., Hearne, S., Huihui Li In: Molecular Ecology v. 28, no. 15, pag. 3544-3560.

22. Impacts of drought-tolerant maize varieties on productivity, risk, and resource use: evidence from Uganda. 2019. Simtowe, F.P., Amondo, E., Marenya, P. P., Rahut, D.B., Sonder, K., Erenstein, O. In: Land Use Policy v. 88, art. 104091.

23. Do market shocks generate gender-differentiated impacts?: policy implications from a quasi-natural experiment in Bangladesh. 2019. Mottaleb, K.A., Rahut, D.B., Erenstein, O. In: Women’s Studies International Forum v. 76, art. 102272.

24. Gender differences in the adoption of agricultural technology: the case of improved maize varieties in southern Ethiopia. 2019. Gebre, G.G., Hiroshi Isoda, Rahut, D.B., Yuichiro Amekawa, Hisako Nomura In: Women’s Studies International Forum v. 76, art. 102264.

25. Tracking the adoption of bread wheat varieties in Afghanistan using DNA fingerprinting. 2019. Dreisigacker, S., Sharma, R.K., Huttner, E., Karimov, A. A., Obaidi, M.Q., Singh, P.K., Sansaloni, C.P., Shrestha, R., Sonder, K., Braun, H.J. In: BMC Genomics v. 20, no. 1, art. 660.