Search

We examine spillovers from agricultural estates to Malawian smallholders within an econometric counterfactual framework. We consider economic spillovers such as income, as well as agrarian spillovers such as yields, harvests, and crop diversity. We identify long-run effects of large agricultural investments on small-scale farmers. For the location of large estates, we use a novel OpenStreetMap dataset, while data on smallholder’s stems from a household survey. We provide evidence for the importance of the distance threshold for spillovers, and explore multiple thresholds. In proximity to estates we find higher groundnut and pigeon pea yields and increased crop diversity. In very close proximity, incomes are also higher. Area under cultivation in total and for maize are smaller for nearby households, while maize yields are not significantly different. Overall, our results suggest that policies should aim to leverage the increased crop diversity and groundnut yields while mitigating potential detrimental effects arising from reduced cultivated land.

In China, irrigation plays a fundamental role in food production, which is hampered by water security, rising population and climate change. To ensure food security and formulate agricultural and irrigation policies, decision-makers need detailed grid-level information on the location of irrigated areas. Unfortunately, this information is not easily available as national irrigation maps are frequently outdated and often have a coarse spatial resolution. In this paper, we present new irrigation maps for China at a spatial resolution of 30 arc seconds (~1 × 1 km) that cover the period between 2005 and 2015. The maps were created using a synergy approach, which combines and integrates regional and global irrigation maps, cropland maps and subnational statistics. The maps were calibrated to subnational irrigation statistics and validated using an external dataset with geocoded information on the location of irrigated areas. The maps show, at the detailed spatial level, how much irrigation expanded over the period of 2005–2015. The proposed synergy approach is flexible and can easily be applied to create irrigation maps for other regions.

This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the mapspamc R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the mapspamc documentation. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.

This repository contains all the input and output data (including maps) related to Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm. In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

Detailed and accurate labor statistics are fundamental to support social policies that aim to improve the match between labor supply and demand, and support the creation of jobs. Despite overwhelming evidence that labor activities are distributed unevenly across space, detailed statistics on the geographical distribution of labor and work are not readily available. To fill this gap, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics, informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The super learners outperformed (n = 6) or had similar (n = 1) accuracy in comparison to best-performing single machine learning algorithms. A comparison with an independent high-resolution wealth index showed that the shares of the four low-skilled occupation categories (91% of the labor force), were able to explain between 28% and 43% of the spatial variation in wealth in Vietnam, pointing at a strong spatial relationship between work, income and wealth. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

Achieving climate neutrality in the European Union (EU) by 2050 will require substantial efforts across all economic sectors, including agriculture. At the same time, an ambitious unilateral EU agricultural mitigation policy is likely to have adverse effects on the sector and may have limited efficiency at global scale due to emission leakage to non-EU regions. To analyse the competitiveness of the EU's agricultural sector and potential non-CO2 emission leakage conditional on mitigation efforts outside the EU, we apply three economic agricultural sector models. We find that an ambitious unilateral EU mitigation policy in line with efforts needed to achieve the 1.5 C target globally strongly affects EU ruminant production and trade balance. However, since EU farmers rank among the most greenhouse gas efficient producers worldwide, if the rest of the world were to start pursuing agricultural mitigation efforts too, economic impacts of an ambitious domestic mitigation policy get buffered and EU livestock producers could even start to benefit from a globally coordinated mitigation policy.

This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators. The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl

Maize is an important staple crop in Ethiopia. Reducing the yield gap - the difference between actual and (water-limited) potential yield - has wide implications for food security and policy. In this paper we combine stochastic frontier analysis of household survey data with agronomic information on (water-limited) potential yield to decompose the maize yield gap in Ethiopia and highlight policy solutions to reduce the yield gap. Our analysis suggests that lack of access to advanced technologies makes up the largest component of the maize yield gap but market imperfections, economic constraints and management constraints are also important determinants. Potentially, maize production can be increased almost fivefold if all these constraints would be addressed simultaneously and the yield gap could be fully closed. Another finding of the paper is measurement issues in the national household survey (LSMS-ISA), a key source of information for scientists to assess agricultural policies in Ethiopia and other African countries. A comparison with results from a crop model suggests a large number of unrealistic values related to key maize input and output variables. Combining economic and agronomic approaches is therefore not only useful to identify policies to reduce maize yield gaps, but also to assess and improve the quality of data-bases on which recommendations are made.

PR, IFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; CRP2, MTID; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), To guide policymaking, decision makers require a good understanding of the long-term drivers of food security and their interactions. Scenario analysis is widely considered as the appropriate tool to assess ‘wicked problems’, such as ensuring global food security, that are characterized by a high level of complexity and uncertainty. This paper describes the development process, storylines and drivers of four new global scenarios that are specifically designed to explore global food security up to the year 2050. To ensure the relevance, credibility and legitimacy of the scenarios, they have been developed using a participatory process, involving a diverse group of stakeholders. The scenarios consist of storylines and a scenario database that presents projections for key drivers, which can be used as an input into global simulation models.

Increasing human demands for water, energy, food and materials, are expected to accentuate resource supply challenges over the coming decades. Experience suggests that long-term strategies for a single sector could yield both trade-offs and synergies for other sectors. Thus, long-term transition pathways for linked resource systems should be informed using nexus approaches. Global integrated assessment models can represent the synergies and trade-offs inherent in the exploitation of water, energy and land (WEL) resources, including the impacts of international trade and climate policies. In this study, we review the current state-of-the-science in global integrated assessment modeling with an emphasis on how models have incorporated integrated WEL solutions. A large-scale assessment of the relevant literature was performed using online databases and structured keyword search queries. The results point to the following main opportunities for future research and model development: (1) improving the temporal and spatial resolution of economic models for the energy and water sectors; (2) balancing energy and land requirements across sectors; (3) integrated representation of the role of distribution infrastructure in alleviating resource challenges; (4) modeling of solution impacts on downstream environmental quality; (5) improved representation of the implementation challenges stemming from regional financial and institutional capacity; (6) enabling dynamic multi-sectoral vulnerability and adaptation needs assessment; and (7) the development of fully-coupled assessment frameworks based on consistent, scalable, and regionally-transferable platforms. Improved database management and computational power are needed to address many of these modeling challenges at a global-scale.

The Indus River Basin covers an area of around 1 million square kilometers and connects four countries: Afghanistan, China, India, and Pakistan. More than 300 million people depend to some extent on the basin's water, yet a growing population, increasing food and energy demands, climate change, and shifting monsoon patterns are exerting increasing pressure. Under these pressures, a “business as usual” (BAU) approach is no longer sustainable, and decision makers and wider stakeholders are calling for more integrated and inclusive development pathways that are in line with achieving the UN Sustainable Development Goals. Here, we propose an integrated nexus modeling framework co-designed with regional stakeholders from the four riparian countries of the Indus River Basin and discuss challenges and opportunities for developing transformation pathways for the basin's future.

This study describes methodologies that have been developed to assess the food security impact of agricultural technologies supplied by stock exchange companies on the basis of publicly available revenue data. The methods have been developed for fertiliser, improved seeds, cooling and packaging. Given the lack of information on key variables at present it is only possible to apply the method for fertiliser to all companies that sell the technology in their portfolio. For the other three technologies additional information is necessary that is not publicly available., Dit rapport beschrijft de methodes die zijn ontwikkeld om publiek beschikbare data over de opbrengsten van beursgenoteerde bedrijven uit de verkoop van agrarische technologieën, te koppelen aan het effect van deze technologieën op voedselzekerheid. De methodes zijn ontwikkeld voor kunstmest, zaden, verpakkingen en koeltechnieken. Enkel voor kunstmest kan op basis van publiek beschikbare data de methode algemeen worden toegepast, de andere drie technologieën vereisen aanvullende data of aannames die niet algemeen beschikbaar zijn.

Increasing human demands for water, energy, food and materials, are expected to accentuate resource supply challenges over the coming decades. Experience suggests that long-term strategies for a single sector could yield both trade-offs and synergies for other sectors. Thus, long-term transition pathways for linked resource systems should be informed using nexus approaches. Global integrated assessment models can represent the synergies and trade-offs inherent in the exploitation of water, energy and land (WEL) resources, including the impacts of international trade and climate policies. In this study, we review the current state-of-the-science in global integrated assessment modeling with an emphasis on how models have incorporated integrated WEL solutions. A large-scale assessment of the relevant literature was performed using online databases and structured keyword search queries. The results point to the following main opportunities for future research and model development: (1) improving the temporal and spatial resolution of economic models for the energy and water sectors; (2) balancing energy and land requirements across sectors; (3) integrated representation of the role of distribution infrastructure in alleviating resource challenges; (4) modeling of solution impacts on downstream environmental quality; (5) improved representation of the implementation challenges stemming from regional financial and institutional capacity; (6) enabling dynamic multi-sectoral vulnerability and adaptation needs assessment; and (7) the development of fully-coupled assessment frameworks based on consistent, scalable, and regionally-transferable platforms. Improved database management and computational power are needed to address many of these modeling challenges at a global-scale.

Agricultural production in Ghana should more than double to fulfil the estimated food demand in 2050, but this is a challenge as the productivity of food crops has been low, extremely variable and prone to stagnation. Yield gap estimations and explanations can help to identify the potential for intensification on existing agricultural land. However, to date most yield gap analyses had a disciplinary focus. The objective of this paper is to assess the impact of crop management, soil and household factors on maize (Zea mays) yields in two major maize growing regions in Ghana through an integrated approach. We applied a variety of complementary methods to study sites in the Brong Ahafo and Northern region. Farm household surveys, yield measurements and soil sampling were undertaken in 2015 and 2016. Water-limited potential yield (Y w ) was estimated with a crop growth simulation model, and two different on-farm demonstration experiments were carried out in 2016 and 2017. There is great potential to increase maize yields across the study sites. Estimated yield gaps ranged between 3.8 Mg ha −1 (67% of Y w ) and 13.6 Mg ha −1 (84% of Y w ). However, there was no consistency in factors affecting maize yield and yield gaps when using complementary methods. Demonstration experiments showed the potential of improved varieties, fertilizers and improved planting densities, with yields up to 9 Mg ha −1 . This was not confirmed in the analysis of the household surveys, as the large yield variation across years on the same farms impeded the disclosure of effects of management, soil and household factors. The low-input nature of the farming system and the incidence of fall armyworm led to relatively uniform and low yields across the entire population. So, farmers’ yields were determined by interacting, and strongly varying, household, soil and management factors. We found that for highly variable and complex smallholder farming systems there is a danger in drawing oversimplified co

The FOODSECURE Scenario Driver Database contains projections for key drivers that, in combination with storylines, describe four scenarios for the assessment of global food security. The database provides information for 19 regions that together have global coverage for the period 2010-2050.