Accumulating evidence suggests that agricultural production could be greatly affected by climate change, but there remains little quantitative understanding of how these agricultural impacts would affect economic livelihoods in poor countries. Here we consider three scenarios of agricultural impacts of climate change by 2030 (impacts resulting in low, medium, or high productivity) and evaluate the resulting changes in global commodity prices, national economic welfare, and the incidence of poverty in a set of 15 developing countries. Although the small price changes under the medium scenario are consistent with previous findings, we find the potential for much larger food price changes than reported in recent studies which have largely focused on the most likely outcomes. In our low productivity scenario, prices for major staples rise 10-60% by 2030. The poverty impacts of these price changes depend as much on where impoverished households earn their income as on the agricultural impacts themselves, with poverty rates in some non-agricultural household groups rising by 20-50% in parts of Africa and Asia under these price changes, and falling by equal amounts for agriculture-specialized households elsewhere in Asia and Latin America. The potential for such large distributional effects within and across countries emphasizes the importance of looking beyond central case climate shocks and beyond a simple focus on yields - or highly aggregated poverty impacts.

There is widespread interest in the impacts of climate change on agriculture in Sub-Saharan Africa (SSA), and on the most effective investments to assist adaptation to these changes, yet the scientific basis for estimating production risks and prioritizing investments has been quite limited. Here we show that by combining historical crop production and weather data into a panel analysis, a robust model of yield response to climate change emerges for several key African crops. By mid-century, the mean estimates of aggregate production changes in SSA under our preferred model specification are - 22, - 17, - 17, - 18, and - 8% for maize, sorghum, millet, groundnut, and cassava, respectively. In all cases except cassava, there is a 95% probability that damages exceed 7%, and a 5% probability that they exceed 27%. Moreover, countries with the highest average yields have the largest projected yield losses, suggesting that well-fertilized modern seed varieties are more susceptible to heat related losses.

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-105 km2) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

In the 21st century, mapping and monitoring the occurrence of soil degradation will be an important component of successful land management. Remote sensing, with its unique ability to measure across space and time, will be an increasingly indispensible tool for assessing degradation. However, much of the recent experience and progress in using remote sensing and other geospatial technologies to map soil degradation is reported outside of the peer-reviewed literature. This motivated the organization of a special collection of papers focused on remote sensing of soil degradation, to highlight recent successes, common challenges, and promising new approaches. This introductory paper provides an overview of the papers, gaps in knowledge, and future research directions. Across several regions and types of degradation, many assessments to date have relied heavily on data from the Landsat satellite sensor. Many approaches have also relied at some point on subjective visual interpretation, either of the satellite imagery itself or to provide field data used to train models that use satellite data. While subjectivity is not necessarily bad, it precludes repeatability and makes it even more important to rigorously test model estimates with independent data. Overall, it remains quite challenging to find robust relationships between remote sensing measures and soil degradation, particularly for slight to moderate levels of degradation. There have nonetheless been some clear successes, and there remains great potential for progress. Promising directions outlined in the papers include using multi-year measures of vegetation condition, combining different sensor systems including optical and radar data, and using advanced statistical techniques such as Bayesian networks and decision trees.

The ongoing expansion of oil palm plantations in the humid tropics, especially in Southeast Asia, is generating considerable concern and debate. Amid industry and environmental campaigners' claims, it can be hard to perceive reality. Is oil palm a valuable route to sustainable development or a costly road to environmental ruin? Inevitably, any answer depends on many choices. But do decision makers have the information they require to avoid pitfalls and make the best decisions? This review examines what we know and what we don't know about oil palm developments. Our sources include academic publications and ‘grey' literature, along with expert consultations. Some facts are indisputable: among these are that oil palm is highly productive and commercially profitable at large scales, and that palm oil demand is rising. Implementing oil palm developments involves many tradeoffs. Oil palm's considerable profitability offers wealth and development where wealth and development are needed-but also threatens traditional livelihoods. It offers a route out of poverty, while also making people vulnerable to exploitation, misinformation and market instabilities. It threatens rich biological diversity-while also offering the finance needed to protect forest. It offers a renewable source of fuel, but also threatens to increase global carbon emissions. We remain uncertain of the full implications of current choices. How can local, regional and international benefits be increased while costs are minimised? While much important information is available, it is often open to question or hard to generalise. We conclude this review with a list of pressing questions requiring further investigation. Credible, unbiased research on these issues will move the discussion and practice forward.

In this chapter, we focus specifically on agricultural risks and uncertainties related to climate variability and global climate change from a policy viewpoint. Policymakers have little control over the weather, which is driven by very short-run (hourly to daily) patterns in atmosphere and ocean circulation. With good scientific information, however, policymakers in many regions can anticipate longer-run (monthly, yearly, decadal) climate variability and climate change reflected in patterns of temperature and precipitation. Such climate fluctuations involve structural dynamics in the physical system that can be modeled and projected with varying degrees of certainty over different spatial and temporal scales. To the extent that climate variability and change in the mean state can be projected, governments can then facilitate adaptation; that is, they can augment markets by implementing policies to promote domestic food security via trade (e.g., arrange for food imports when crop production is expected to decline domestically), investments (e.g., fund crop research or improvements in irrigation infrastructure), and early-warning systems or safety-net programs for vulnerable populations within their countries.