KEY MESSAGES

A first step in evaluating the effects of agricultural investments in developing countries to recognize that policy makers will almost certainly have multiple objectives.

Agricultural development is crucial in developing countries, and particularly in the poorest countries where it accounts for large shares of employment and income and whose poverty is due simply to having a large share of the workforce in low-prod

Some agricultural investments are commodity-specific, meaning that they increase the productivity of production, processing, or marketing of a single agricultural commodity or a set of closely-related commodities.

Agriculture is fundamental to all three pillars of sustainability, environment, society, and economy. However, the definition of sustainable agriculture and the capacities to measure it remain elusive.

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming.

Projecting future demand for livestock-derived foods depends on interactions between income, prices, and the income elasticity of that demand.

Vast amounts of resources are spent on support to agriculture, with questionable results for agriculture, for national incomes, for nutrition and for the environment.

The economic crisis and food and health system disruptions related to the COVID-19 pandemic threaten to exacerbate undernutrition in low- and middle-income countries (LMICs).

Background: Climate change presents an increasing challenge for food-nutrition security. Nutrition metrics calculated from quantitative food system projections can help focus policy actions.

Substantially reducing GHG emissions from agriculture while safeguarding food security requires a more comprehensive revamping of existing support to agriculture & food consumption.

The food system, and those who depend on it, have been strongly but unevenly affected by COVID‐19.

This study assesses the impact of coronavirus disease 2019 (COVID‐19) on poverty, food insecurity, and diets, accounting for the complex links between the crisis and the incomes and living costs of vulnerable households.

This report evaluates the outputs from the CGIAR Research Program on Policies, Institutions, and Markets (PIM) on national social accounting matrices (SAMs) and single-country computable general equilibrium (CGE) models.

This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods.

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change.

Climate change effects on agricultural yields will be uneven over the world. A few countries, mostly in high latitudes, may experience gains, while most will see average yield decrease.

Strategic foresight is systematic means to explore plausible futures.

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 environme

This study assesses the impact of COVID-19 on poverty, food insecurity and diets, accounting for the complex links between the crisis and the incomes and living costs of vulnerable households.