Frontier Analysis and Agricultural Typologies

Maruyama, Eduardo; Torero, Máximo; Scollard, Phoebe; Elias, Marlène; Mulangu, Francis; Seck, Abdoulaye

doi:10.2139/ssrn.3156944

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…This scenario is informed by the results from the agricultural typology analysis (Maruyama et al 2018). Maruyama et al (2018) constructed agricultural typologies of microregions in Burkina Faso to identify micro-regional level opportunities, bottlenecks and investment gaps based on the concept of the production possibilities frontier applied to farm activities, drawing on highly detailed household-level survey and geospatial data on agroecological conditions, accessibility and poverty. This typology scenario considers that all farmers move from their current level of efficiency and the annual productivity growth rate by AEZ required to catch up with the frontier is calculated.…”

Section: Improvement Of Agricultural Production Efficiencymentioning

confidence: 99%

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The Case of Burkina Faso

2019

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractIn this study, we develop an economy wide model for Burkina Faso to assess the most promising opportunities for technological innovations to enhance maize production and productivity and their economywide effects. We simulate the implementation of two agricultural technological innovations using a customized Computable General Equilibrium (CGE) model. One innovation is an improvement of famers' efficiency, i.e. operating on the production frontier (typology scenario). The other shifts the frontier itself and involves the introduction of a new cultivar (crop scenario). The model has been made agriculture-focused through the following features: separate agriculture and non-agriculture labor markets, separate urban and rural representative household groups, including welfare analysis and the imperfect integration of land markets, i.e. the land market is split into agroecological zones (AEZs). The CGE model is a single-country, multi-sector, multi-market model and solved for multiple periods in a recursive manner, ten years in the case of Burkina Faso. The CGE model is calibrated using a 2013 Social Accounting Matrix (SAM). The SAM has several interesting features with regards to agricultural modelling and highlights the focus crops for Burkina Faso and particularly maize, which is the focus crop of this study. The results showed prospects of gains for the economy with the introduction of technological innovations in the maize value chain in Burkina Faso. Welfare analyses performed showed welfare gains for all household profiles studied. In other words, the introduction of innovations in the maize value chain seems to be pro-poor. Finally, the study found that a total increase of about 2% of public expenditure in this sector over 10 years is required to achieve the simulated results.

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Section: Improvement Of Agricultural Production Efficiencymentioning

confidence: 99%

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The Case of Burkina Faso

2019

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“…The objective is to estimate the growth rate in productivity needed to catch up with the production frontier. Maruyama et al (2018) constructed agricultural typologies of micro regions in Malawi to identify micro-regional level opportunities, bottlenecks and investment gaps based on the concept of the production possibilities frontier applied to farm activities, drawing on highly detailed household-level survey and geospatial data on agro ecological conditions, accessibility and poverty. 11 This typology scenario considers that all farmers move from their current level of efficiency and the annual productivity growth rate by AEZ required to catch up with the frontier is calculated.…”

Section: Typology Scenariomentioning

confidence: 99%

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The case of Malawi

2019

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This discussion paper analyzes the economy-wide impact of a series of agricultural innovations in Malawi. Using an agricultural focused computable general equilibrium model disaggregated to reflect Malawi agro-ecological zones, we simulate three scenarios: one involving smallholders catching up to the production frontier, and two scenarios of agronomic innovations consisting in changes in level and application rate of nitrogen. Our results show a positive impact on the economy following an increase in maize yield and production efficiency under the different scenarios.

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“…T A B L E 2 HiH typologies in Ecuador and associated recommended interventions adapted fromMaruyama et al (2018).…”

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confidence: 99%

An interactive system to map land degradation and inform decision‐making to achieve land degradation neutrality via convergence of evidence across scales: A case‐study in Ecuador

Teich

Harari

Caza³

et al. 2023

Land Degrad Dev

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One of the core challenges to achieve Land Degradation Neutrality (LDN) is to spatially identify, and strategically prioritise, the areas to implement actions to avoid, reduce and reverse land degradation. To achieve this, a tool for a participatory and data‐driven assessment considering both the biophysical, and socio‐economic dimensions of land degradation across scales was developed for Ecuador. In this paper, we present the methodology and results obtained, including the spatially explicit interactive tool developed to integrate indicators that support the scaling‐up of sustainable land management (SLM). The process involved specialists from various national and international institutions, as well as decision makers from the public sector and other relevant stakeholders. Cloud computing allowed the integration of five main sources of data: (1) the results of a participatory land degradation assessment based on an expert knowledge questionnaire following the Land Degradation Assessment in Drylands (LADA) and World Overview of Conservation Approaches and Technologies (WOCAT) methodology; (2) the Hand‐in‐Hand Initiative Ecuador typology maps based on poverty and estimated agricultural potential and efficiency scores from household surveys; (3) National data sets on land cover and land use, soil properties, and hydro climatic indicators; (4) global satellite‐derived LDN indicators, such as Land Productivity Dynamics; and (5) Documented SLM practices from WOCAT Global SLM Database. The tool is based on a Google Earth Engine application and allows decision makers to easily compare results and obtain statistics at different spatial scales and landscapes, including Land Use Systems delimited by experts. It also includes a multi‐criteria module to identify areas with specific characteristics to prioritise different types of interventions to achieve the country's LDN targets. Convergence of local and global evidence allowed the identification of hotspots of degradation as well as areas of false positives/negatives ‐ if only global or remote sensing indicators were considered. The participatory process contributed to strengthening multi‐sector cooperation mechanisms and to guaranteeing ownership of the tool and the results. The system will support Ecuador's efforts to monitor and report progress towards LDN to the United Nations Convention to Combat Desertification. The system's code is shared as a repository at Earth Engine and can be adapted to and used by other countries and regions.This article is protected by copyright. All rights reserved.

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Frontier Analysis and Agricultural Typologies

Cited by 4 publications

References 32 publications

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The Case of Burkina Faso

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The Case of Burkina Faso

Modelling the Economy-Wide Impact of Technological Innovation and Mapping Agricultural Potential: The case of Malawi

An interactive system to map land degradation and inform decision‐making to achieve land degradation neutrality via convergence of evidence across scales: A case‐study in Ecuador

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