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PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; Foresight and Policy Modeling (FPM); Transformation Strategies, Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may lack sufficient local variation in the economic activities to analyze local economic development patterns and the exposure to natural hazards. Agriculture GDP is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend on their livelihoods that provide a key source of income for the entire household (FAO, 2021). Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP is an estimated US$432 billion of agricultural GDP circa 2010, where nearly 1.2 billion people live. The data are available on the World Bank Development Data Hub (DOI: http://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022).

PR, IFPRI3; ISI; DCA; CRP2; 4 Transforming Agricultural and Rural Economies; BioRAPP, PIM; EPTD, CGIAR Research Program on Policies, Institutions, and Markets (PIM), An enabling, evidence-based decision-making framework is critical to support agricultural biotechnology innovation, and to ensure farmers’ access to genetically modified (GM) crops, including orphan crop varieties. A key element, and often a challenge in the decision making process, involves the balancing of identified potential risks with expected economic benefits from GM crops. The latter is particularly challenging in the case of orphan crops, for which solid economic data is scarce. To address this challenge, the International Food Policy Research Institute (IFPRI) in collaboration with local economists analyzed the expected economic benefits to farmers and consumers from the adoption of GM crops in 5 sub-Saharan African countries. This paper focuses on case studies involving insect resistant cowpea in Nigeria and Ghana; disease-resistant cassava in Uganda and Tanzania; and disease-resistant banana in Uganda. Estimations from these case studies show substantial economic benefits to farmers and consumers from the timely adoption and planting in farmers’ fields of GM orphan crops. Our analysis also shows how the benefits would significantly be reduced by regulatory or other delays that affect the timely release of these crops. These findings underscore the importance of having an enabling policy environment and regulatory system—covering, among other elements, biosafety and food/feed safety assessment, and varietal release registration—that is efficient, predictable, and transparent to ensure that the projected economic benefits are delivered and realized in a timely manner.

PR, IFPRI3; DCA; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply, EPTD; PIM, CGIAR Platform for Big Data in Agriculture (Big Data); CGIAR Research Program on Policies, Institutions, and Markets (PIM), Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V; IFPRI, 2019).

PR, IFPRI3; ISI; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies, EPTD

Non-PR, IFPRI1; CRP2; PBS; DCA; 4 Transforming Agricultural and Rural Economies; BioRAPP, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Ethiopian economy has grown at an average rate that surpasses that of almost any other economy in the region over the last two decades. At the center of this development is the high priority placed on accelerating agricultural growth and achieving food security and poverty alleviation. Over the years, maize has become a main food security crop, widely produced and consumed by smallholder farmers, second only to teff in terms of area. Despite the sustained growth of maize production over the years, its yields continue to be lower than the world’s average. Of the many abiotic and biotic constraints that maize faces, insect attacks and droughts are two critical ones. The genetically modified TELA maize can help address these constraints. This paper estimates the economic benefits of adopting this new technology and the opportunity cost that Ethiopia will incur if its adoption is delayed. The analysis is conducted using an economic surplus partial equilibrium model run with the newly developed DREAMpy software, data drawn from the Ethiopia Socioeconomic Survey, Wave 3 2015-2016, econometric estimations using these survey data, and other local data and sources. The estimations show that if the drought tolerant and insect resistant TELA maize is planted in 2023 the net present-value of benefits for producers and consumers would be around $850 million. Producers from the mid-altitude maize zone will be the main beneficiaries, given the targeted area of TELA maize. Consumers from all areas will benefit from the projected reduction in price. If the adoption of this new technology is delayed by 5 years, the estimated net present value of benefits will fall by 30 percent. These costs underscore the importance of having a regulatory system that is efficient, predictable, and transparent and ensures that the projected economic benefits are realized.

Non-PR, IFPRI1; CRP2; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; Capacity Strengthening; BioRAPP, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), The Government of Uganda has implemented programs and policies to improve the agricultural sector’s recent underperformance. Uganda’s two main food security crops, bananas and cassava, have been critically affected by two diseases: Banana Xanthomonas Wilt (BXW) and Cassava Brown Streak Disease (CBSD). The effectiveness of agronomic and cultural practices to control these diseases has been limited, requiring better alternatives. The Ugandan R&D sector in collaboration with international partners have developed genetically engineered innovations that can control both diseases. To examine the potential benefits to consumers and producers from the adoption of genetically engineered banana and cassava with resistance to BXW and CBSD, we use a set of economic impact assessment methods. These include an economic surplus model implemented via IFPRI’s DREAMpy framework, a real options model and a limited gender assessment. Results from the economic surplus approach suggest that the adoption of both technologies can benefit Uganda. These results were confirmed for the case of bananas and partially for the case of cassava using the real options and the gender assessment performed. Results from this assessment are predicated on Uganda maintaining an enabling environment that will ensure the deployment and use of both innovations. Looking forward, continuing to improve enabling environment for innovation in Uganda will require addressing current R&D, regulatory, technology deployment and product stewardship processes constraints.

Non-PR, IFPRI1; CRP2; DCA; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance; G Cross-cutting gender theme; Capacity Strengthening; PBS, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Despite agriculture’s key role in Tanzania, agricultural productivity has remained relatively low compared with that of most other countries producing similar crops globally. Recent innovations in the sector such as development of genetically modified (GM) crop varieties with traits targeted to specific contextual challenges could revolutionize the country’s agricultural performance. Tanzania is attempting to deploy drought- and pest-resistant (WEMA) maize as well as brown-streak-disease-resistant cassava varieties. But little is known, contextually, about potential economic impacts of these crop varieties on Tanzanian farmers and consumers. This study implements an ex ante impact assessment to answer such important policy questions. Using DREAM, a model that estimates economic surplus as projections of consumer and producer gains from the use of a technology, complemented with locally collected and validated data, we document positive net economic impacts from the potential adoption and use of both maize and cassava GM varieties. Results are robust to various sensitivity tests and methodological cross checks that consider a range of values for production markets, performance, and adoption assumptions. Adoption of a GM crop is predicated on compliance with regulatory and other governance requirements, proper product dissemination and stewardship, and the technology’s effectiveness in addressing producer productivity issues. Special attention needs to be paid to reducing regulatory and governance delays so as to minimize inefficiencies and potential coordination issues that may arise over time.

Non-PR, IFPRI1; CRP2; 4 Transforming Agricultural and Rural Economies; PBS; DCA, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Since oil prices’ decline in 2014, agriculture has received renewed interest in Nigeria as a key sector for achieving sustainable growth and generating foreign exchange. One of the identified obstacles to achieving these goals is the need to improve agricultural productivity. Cowpea is one of the priority crops identified for productivity improvement. Currently cowpea yields are below 900 kg/ha, but it has been shown that with the right technology, these yields could potentially double. One of the main biotic constraints for cowpea is the infestation of the insect pod borer (Maruca Vitrata). No conventional variety has been developed to resist this pest, but with the use of biotechnology and the sustained collaboration of national and international partners over many years, there is now a genetically modified pod-borer-resistant (or more generally insect-resistant) cowpea. This paper estimates the potential economic benefits of adopting this new technology and the cost that Nigeria will incur if this adoption is delayed. The analysis is conducted using an economic surplus partial equilibrium model run with the newly developed DREAMpy software, data drawn from the Nigeria General Household Survey 2015–2016, estimations using these data, and other local sources. The estimations show that if the insect-resistant cowpea is planted in 2020, the net present-value benefits for producers and consumers would be around US$350 million, 70 percent of which would be accrued by producers. The distribution of benefits by region show that Sudan-Sahel will accrue the most benefits, given the relative concentration of cowpea in this region and the estimated higher adoption rates and yield changes. Almost half of producers’ total benefit will go to large producers, who represent only 20 percent of all cowpea producers, while small producers, representing half of all cowpea producers, will receive 24 percent of the benefit. Additionally, the analysis shows that a five-year regulatory d

Non-PR, IFPRI5; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; CRP2, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with nationaland/or subnational-level data.

PR, IFPRI3; HarvestChoice; ISI; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Worldwide, crop production is intrinsically intertwined with biological, environmental and economic systems, all of which involve complex, inter-related and spatially-sensitive phenomena. Thus knowing the location of agriculture matters much for a host of reasons. There are several widely cited attempts to model the spatial pattern of crop production worldwide, not least by pixilating crop production statistics originally reported on an areal (administrative boundary) basis. However, these modeled measures have had little scrutiny regarding the robustness of their results to alternative data and modeling choices. Our research casts a critical eye over the nature and empirical plausibility of these types of datasets. To do so, we determine the sensitivity of the 2005 variant of the spatial production allocation model data series (SPAM2005) to eight methodological-cum-data choices in nine agriculturally-large and developmentally-variable countries: Brazil, China, Ethiopia, France, India, Indonesia, Nigeria, Turkey and the United States. We compare the original published estimates with those obtained from a series of robustness tests using various aggregations of the pixelized spatial production indicators (specifically, commodity-specific harvested area, production quantity and yield). Spatial similarity is empirically assessed using a pixel-level spatial similarity index (SSI). We find that the SPAM2005 estimates are most dependent on the degree of disaggregation of the underlying national and subnational production statistics. The results are also somewhat sensitive to the use of a simple spatial allocation method based solely on cropland proportions versus a cross-entropy allocation method, as well as the set of crops or crop aggregates being modeled, and are least sensitive to the inclusion of crude economic elements. Finally, we assess the spatial concordance between the SPAM2005 estimates of the area harvested of major crops in the United States and pixelated me

Non-PR, IFPRI1; DCA; CRP2; 4 Transforming Agricultural and Rural Economies; Capacity Strengthening; BioRAPP, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), This paper uses an innovative research process to quantify the potential impacts of releasing and adopting insect-resistant (IR) cowpea and nitrogen-use efficient (NUE) rice in Ghana using an economic surplus partial equilibrium model. The premise of the research process was to build national capacity to produce timely and robust estimates, based on secondary data and qualified experts’ informed opinions, collected in country. Ghana’s stakeholders selected the two genetically modified (GM) technologies discussed here based on their assessment of these GM products’ regulatory advancement and their economic and political importance. Using assumptions regarding the expected changes from the adoption and commercialization of these crops, collected from national and international crop and technology experts, the authors estimate that the benefits of adopting IR cowpea are between US$5.5 million and US$125.3 million, and between US$1.9 million and US$153 million for NUE rice. The analysis also shows how a five-year regulatory delay may erode these benefits, reducing them by between 29 and 39 percent for IR cowpea and between 28 and 57 percent for NUE rice. Additionally, the authors make preliminary estimates of sex-disaggregated benefits and calculate the unequal distribution of benefits between female and male producers and consumers owing to gender disparities in production and consumption. The welfare estimations are based on an economic surplus model that were estimated using the DREAM software. Although this partial equilibrium model has limitations regarding market-clearing assumptions and is specific to a product, it is a data-parsimonious method that can produce results in a short time frame, which might better suit policymakers’ and decision makers’ demands for rapid estimations.

PR, IFPRI3; ISI; IFPRIOA; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; SATISFy; HarvestChoice, EPTD; PIM, CGIAR Research Programs on Policies, Institutions, and Markets (PIM), Our simulation study examined the productivity and economiceffects of planting different seed cultivars and increasing fertilizer application rates at multiple spatial scales formaize in Tanzania. We combined crop simulation modelling with household data on costs and prices to examinefield-scale and market-scale profitability. To scale out our analysis from the field scale to the regional andnational scale (market scale) we applied an economic surplus model.

IFPRI1; Open Access, EPTD, CGIAR Platform for Big Data in Agriculture (Big Data), Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM, also known as MapSPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating Africa South of the Sahara-wide grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

IFPRI1; Open Access; CRP2; HarvestChoice, EPTD; PIM, Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

IFPRI1; Open Access; CRP2; HarvestChoice, EPTD; PIM, CGIAR Platform for Big Data in Agriculture (Big Data); CGIAR Research Program on Policies, Institutions, and Markets (PIM), Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts

Non-PR, IFPRI3; CRP2; A Ensuring Sustainable food production; HarvestChoice, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Non-PR, IFPRI1; HarvestChoice; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), By 2050, world population is estimated to reach 9.7 billion people (United Nations 2015). The increased food, fiber and fuel demand from this population will significantly affect land and resource use, climate change, the nature and prevalence of poverty, political agendas and technological development. Preemptively addressing the effects of this increased demand is aided by a clear and reliable understanding of the current spatial distribution of cropping systems in the world. The Spatial Production Allocation Model (SPAM) 2005 endeavors to disaggregate crop statistics identified at national and sub-national units for the year 2005 to 5 arc-minute grid cells while taking account of different farming practices. SPAM generates four major output variables: physical area, harvested area, production quantity and yield for each of 42 crops distinguished by four production systems (i.e., irrigated – high input, rainfed – high input, rainfed – low-input and rainfed – subsistence production). The allocation model uses a cross-entropy optimization approach informed by major inputs such as cropland surface, location of irrigated areas, crop suitability and potential yields, rural population densities, production systems characteristics and crop prices to disaggregate crop statistics. Our primary intent is to document the data sources used to compile the SPAM2005 estimates, to outline the major features of the model itself, as well as the adjustments and modifications undertaken to generate these plausible spatial estimates of crop area, production and yield.

PR, IFPRI1; CRP2; ReSAKSS; D Transforming Agriculture, DSGD; EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), The previous chapter examined several measures of productivity, primarily in terms of their evolution over time, and reported those changes by countries and subregions. However, the conditions under which agriculture is practiced and specific production systems predominate are highly diverse spatially, even within a single country. This chapter, therefore, examines patterns of agricultural productivity not only at a greater spatial resolution but also in terms of the spatial distribution of specific production systems. We first summarize some of the reasons for growing interest in the spatial dimensions of agriculture, briefly review the general characteristics of the spatial datasets used, and then describe the specific production system schema underpinning the analyses presented in this and subsequent chapters. In the following sections we describe the spatial variability of key factors shaping the productivity of production systems, examine the overall value of (crop) production and associated spatial patterns of land and labor productivity, and briefly discuss the projected effects of climate change spatially. In the final section, we summarize our findings and their implications for prioritizing and targeting interventions, especially in the context of planning for knowledge and technology spillover across domains, countries, and subregions.

Non-PR, IFPRI1, EPTD, The infographic shows the visualization of national areas and type of GM crops planted in 2013 using Clive James ISAAA Brief 46 Global Status of Commercialized Biotech/GM Crops: 2013. It also uses World Bank (WDI 2014) income level classification. For detailed GM crop data, visit http://www.isaaa.org/resources/publications/briefs/46/default.asp

PR, IFPRI3; ISI; CRP7; CRP2, EPTD; PIM, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Non-PR, IFPRI5; CRP2, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; HarvestChoice; CRP2, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI3; ISI; HarvestChoice; CRP2, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

PR, IFPRI1; HarvestChoice; CRP2, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)