BioSight: Understanding tradeoffs in agricultural intensification

Technological dynamism is key to improving the lives of poor farmers worldwide. Understanding the impacts of adoption of superior technology and sustainable managerial practices is a critical first step to helping farmers succeed. PIM supports activities to improve the tools for assessment of technology adoption and analyzing tradeoffs associated with new technologies.

The BioSight project, initiated in early 2013 with funding from PIM, seeks to build a strong analytical framework for understanding and managing the tradeoffs emanating from land use and technological choice that have consequences for environmental quality, resource sustainability, and socio-economic well-being.

In this interview, the BioSight’s lead Dr. Siwa Msangi tells about the project and its objectives. 

- What specific tradeoffs do you study and why?

Let’s take a parliamentarian facing debate on pending regulations that affect use of land for agriculture. Her constituents want to make more money, but she knows that preserving the natural cover and biodiversity of the land is important to protect watersheds and future production.  She needs tools that allow her to weigh expansion of cultivated area in her jurisdiction versus intensification through increased use of inputs such as fertilizers. The inputs could have environmental consequences: potential run-off into rivers and lakes, and increased emissions of greenhouse gases. Our models can help the parliamentarian evaluate the implications of the regulation for producer incomes and the health of the watershed, and also to see how different formulations of the proposed regulations might achieve both goals.

A farmer within the parliamentarian’s constituency would probably look at the tradeoffs from a different perspective.  We can use our tools to assess whether adoption of new technology to boost yields is in the farmer’s interest, and also how different formulations of the regulations might encourage him to intensify and at the same time avoid soil erosion and preserve a protected space along the river that runs through his plot.  Rural advisors using a simplified set of tools from our work can help a farm household evaluate potential technologies and management practices.

- The tools sound very useful.  What exactly are they? 

Farmers examines the seed

Photo: Apollo Habtamu/ILRI

Our models link biophysical inputs and outputs of crops and livestock to economic models of behavior. We build them in a way that allows us to capture the diversity of farm types. For example, in the work that we are doing in Malawi, we are partnering with the IFPRI team working on the USAID-funded Africa RISING* project. We use the data collected in key provinces to construct a household-level farm production model that ties production decisions more closely to household income and consumption decisions for various farm-household types, and helps capture observed behavior and assess potential responses to different policies and technical options.

For example, we can model the introduction of irrigation into a rainfed farming system, or the use of different types of seed technology (which could be higher-producing or more resilient).

- Do you use available data or collect new primary data?

At this point we are working with available data.  The  Africa RISING project has collected detailed information about household characteristics and production behavior, in terms of cropping choice, input use, labor allocations, etc. – i.e. all the data that we can use to parameterize and calibrate our models so that they can better represent both the potential development and the reality that we observe in the field.

- How do you communicate with users of your tools? 

RichardHowitt_Lecturing_OpeningSession1

Modeling training for agricultural economists, World Agroforestry Center, Kenya, May 2014

At this point we are training agricultural economists and specialized analysts in the design and application of agricultural production and resource management models. We are also developing simplified tools and materials that advisors can use, together with some case studies illustrating applications.

- What are you working on now?

Right now we are focusing on the case studies in Malawi, and particularly trying to improve the representation of interactions between crops and livestock within systems. We are trying to get better information on farm-level animal production, so that we can characterize livestock productivity as a function of the feeding choices that the farmers make. The data about the inputs that go into livestock are often not as detailed as the information that we normally see about crop production, so some additional survey work is needed.  We want to strengthen our partnership with ILRI (International Livestock Research Institute) on this. We would also like to do more on integrating representation of aquaculture into the farm-level models. For example, in Bangladesh, Vietnam and some other countries aquaculture has been used in rotation with rice production providing additional income to farmers.

- How is your work different from the foresight modeling work, also under PIM portfolio?

We focus primarily on micro-level farm production behavior that is observed under particular farming systems, and tie it to the consumption decisions of households. We characterize farm-level decision processes, identifying, for example, typologies of farmers, so that we can make a clear distinction between those farm households who are more resource-constrained, and have less access to assets and inputs versus those who are more commercialized and have a wider range of flexibility in their production choices. We can zero in on detail about specific decisions, such as labor usage, irrigation choices, and some of the household income level dynamics – in order to focus more on questions of long-term investment and impacts.

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Photo: Neil Palmer/CIAT

Our micro-level approach allows us to examine gender issues more closely.  For example, we can see how key production decisions are undertaken differently by men and women in the household, and how the benefits and costs of technology or policy-focused interventions might be perceived differently by men and women.  We are also working to model trade-offs that women face in choosing to work on the household land or instead to take wage work off the farm. Ultimately we want to link our micro-level work to the more macro approaches that the foresight models take.  We could, for example, impose a different climate regime on our farm household production models, and observe the possible responses and outcomes. Our work at the household level will help the foresight modeling team refine their assumptions about adoption of technology and the longer term implications of that adoption on factors such as soil fertility and water resources.  The two approaches are thus complementary.

- The emphasis on gender is quite innovative.  Where do you get the data for this work?

Some data on gender are available from the household production surveys. For example, the data set that we get for Malawi from the Africa RISING project contains sex-disaggregated information showing whose labor (men’s or women’s) has been contributed to the production of a certain crop, so we have an understanding about the division of labor used for, say, plowing and weeding. Weeding is often women’s work. Applying more fertilizer usually produces more weeds, with clear implications for women relative to men.  This is just one example of gender-specific insights that emerge from modeling tradeoffs at the household level.  Turning to another example, some of the decline in agricultural production in households affected by the tragedy of Ebola in West Africa may be due to the increased burden of healthcare, which tends to fall mostly on women, and interferes with their time spent in the fields.  We do not need complex models to understand the implications of a shock such as Ebola, but modeling the constraints on women’s time under more normal conditions can yield important insights into options for promoting sustainable intensification in a way that brings equitable benefits to men and women.

 

Related links:

BioSight page on the IFPRI site

Blog posts:

BioSight workshop: building a framework for modeling sustainable agricultural intensification (Dec 2013)

Of beneficial insects and behavioral games (Dec 2014)

 

Featured image: Panoramic view of farmer's field under conservation agriculture in Malawi. Photo credit: T. Samson/CIMMYT, Flickr


* RISING = Research In Sustainable Intensification for the Next Generation

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