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HarvestChoice

HarvestChoice is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand HarvestChoice rather than just read about it. In short: HarvestChoice is a research initiative, which generates information to help guide strategic investments in agriculture aimed at improving the well-being of poor people in Sub-Saharan Africa through more productive and profitable farming. The initiative is coordinated by the International Food Policy Research Institute and the University of Minnesota and is supported by a grant to IFPRI by the Bill & Melinda Gates Fo…

Key takeaways

  • HarvestChoice belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect HarvestChoice to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of HarvestChoice from memory before moving on to harder problems.

Reference excerpt

HarvestChoice is a research initiative, which generates information to help guide strategic investments in agriculture aimed at improving the well-being of poor people in Sub-Saharan Africa through more productive and profitable farming. The initiative is coordinated by the International Food Policy Research Institute and the University of Minnesota and is supported by a grant to IFPRI by the Bill & Melinda Gates Foundation. Phase I of HarvestChoice ran from October 2006 to June 2010, while Phase II began in December 2010 for a period of 4 years and a total budget of $8.2M.

Purpose of the Initiative HarvestChoice and its partners develop databases, tools, analyses, and syntheses designed to improve strategic investment and policy decisions related to farming. The overriding objective is to accelerate and enhance the performance of those crops and cropping systems most likely to bring significant benefits to the world's poor and undernourished.

Types of Information provided The use of spatially‐referenced data and spatially‐explicit analysis to generate spatially specific knowledge is a cornerstone of the HarvestChoice initiative. A fundamental characteristic of agriculture (particularly subsistence agriculture) is the close coupling of its performance with prevailing biophysical conditions, conditions that can vary widely over space and time. HarvestChoice relies on its own and its partners' spatial datasets to provide new information on:

the location of the poor and undernourished in relation to major crop production systems the dependence of both urban and rural poor on specific crops and crop products the incidence and severity of major production constraints such as drought and disease in focus crops and locations the potential benefits to the poor from alleviating such constraints an inventory and characterization of existing and prospective technologies that might help address an economic evaluation of the potential crop production, consumption, price, and trade, as well as the likely hunger and income consequences of a range of technology scenarios, and the potential commercialization challenges that promising technology options might face.

Types of spatial data There are five major, intertwined geographies of direct relevance to the work of HarvestChoice;

the spatial distribution and performance of agricultural production systems, the spatial distribution and severity of production constraints (e.g., drought, low fertility soils, pests and diseases), spatial variation in the potential efficacy of on-farm interventions (e.g., improved seeds, mulching, supplemental irrigation, fertilizer use, biological control of pests), spatial variation in access to input and output markets (e.g. time of travel to markets, farmgate prices of fertilizer and agricultural products), spatial variation in national and local policies and regulations (that influence, for example, marketing decisions, the quality of infrastructure and services, the generation of and access to, and uptake of new technology).

Spatial products HarvestChoice makes available spatially (and socio-economically) explicit estimates of the potential welfare benefits of a range of interventions (e.g., on-farm, market and market access, and national policy). These maps (alongside tables, graphs, and text) provide information of direct relevance to agricultural development investors and policymakers. They do this by detailing the potential scale and distribution of economic benefits – including the identification of locations and social groups whose welfare might be impacted negatively. These outputs will, however, be supplemented by a larger collection of novel spatial data products that represent key, intermediate factors;

the baseline location and performance of agriculture worldwide (for at least 15 crops), the incidence and severity of production constraints (for at least 30 abiotic and biotic constraints), the potential response to on-site technology and management interventions (e.g., for different crop varieties, different levels of nutrient, water, and pest management), and the influence of changing transportation costs on profitability. This amounts, potentially, to several thousand maps and associated datafiles.

References

Literature Alston, Julian M., P.G. Pardey, J.S. James, and M.A. Andersen. 2009. The economics of agricultural R&D. Annual Review of Resource Economics 1: 537-565. Wood, S. 2007. A Harvest Choice Primer on Agricultural Potential Wood, S. 2007. A Harvest Choice Primer on Geographical Targeting/ Segmentation and Development Domains You, L., and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems 90(1-3): 329-347. You, L., S. Wood, and U. Wood-Sichra. 2009. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural System 99, Issues 2-3, p. 126-140.

External links HarvestChoice

Worked examples

Example 1 — a first encounter with HarvestChoice

Start with the simplest possible case. Write down what HarvestChoice claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to HarvestChoice before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about HarvestChoice ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of HarvestChoice

In research
HarvestChoice appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses HarvestChoice in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
HarvestChoice is common in secondary-school and first-year university syllabi. It links to neighbouring topics Agricultural research, Geographic information systems, Statistical data sets, so understanding it makes those chapters shorter.
In everyday life
Look for HarvestChoice outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study HarvestChoice in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what HarvestChoice means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain HarvestChoice out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is HarvestChoice in simple terms?

HarvestChoice is a research initiative, which generates information to help guide strategic investments in agriculture aimed at improving the well-being of poor people in Sub-Saharan Africa through more productive and profitable farming. The initiative is coordinated by the International Food Polic…

Why does HarvestChoice matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study HarvestChoice?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on HarvestChoice.

Tags

  • Agricultural research
  • Geographic information systems
  • Statistical data sets

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