Successful adaptation of agriculture to ongoing climate changes would help to maintain productivity growth and thereby reduce pressure to bring new lands into agriculture. In this paper we investigate the potential co-benefits of adaptation in terms of the avoided emissions from land use change. A model of global agricultural trade and land use, called SIMPLE, is utilized to link adaptation investments, yield growth rates, land conversion rates, and land use emissions. A scenario of global adaptation to offset negative yield impacts of temperature and precipitation changes to 2050, which requires a cumulative 225 billion USD of additional investment, results in 61 Mha less conversion of cropland and 15 Gt carbon dioxide equivalent (CO₂e) fewer emissions by 2050. Thus our estimates imply an annual mitigation co-benefit of 0.35 GtCO₂e yr⁻¹ while spending $15 per tonne CO₂e of avoided emissions. Uncertainty analysis is used to estimate a 5–95% confidence interval around these numbers of 0.25–0.43 Gt and $11–$22 per tonne CO₂e. A scenario of adaptation focused only on Sub-Saharan Africa and Latin America, while less costly in aggregate, results in much smaller mitigation potentials and higher per tonne costs. These results indicate that although investing in the least developed areas may be most desirable for the main objectives of adaptation, it has little net effect on mitigation because production gains are offset by greater rates of land clearing in the benefited regions, which are relatively low yielding and land abundant. Adaptation investments in high yielding, land scarce regions such as Asia and North America are more effective for mitigation.

To identify data needs, we conduct a sensitivity analysis using the Morris method (Morris 1991 Technometrics 33 161–74). The three most critical parameters for improving estimates of mitigation potential are (in descending order) the emissions factors for converting land to agriculture, the price elasticity of land supply with respect to land rents, and the elasticity of substitution between land and non-land inputs. For assessing the mitigation costs, the elasticity of productivity with respect to investments in research and development is also very important. Overall, this study finds that broad-based efforts to adapt agriculture to climate change have mitigation co-benefits that, even when forced to shoulder the entire expense of adaptation, are inexpensive relative to many activities whose main purpose is mitigation. These results therefore challenge the current approach of most climate financing portfolios, which support adaptation from funds completely separate from—and often much smaller than—mitigation ones.

Rapid population growth, urbanization and rising incomes will present an unprecedented opportunity for growth of commercial agriculture and agribusiness in coming years. The value of food consumed in urban areas is set to expand by four times to 2030, but given evidence of a continuing decline in competitiveness much of this could be sourced from imports even in countries with an apparent comparative advantage in agriculture. At the same time, the number of youth entering the labor force will rise to 25 million annually by 2025 putting tremendous pressure on job creation, especially through agriculture. Rising investments in large-scale farming seen in recent years may contribute to increased food supply (although this is highly uncertain given the track record) but some investment, especially in mechanized grain farms, provide few jobs. Even so there is a dire need for increased investment in the sector, both public and private, if it is to realize its potential for growth and poverty reduction.

This paper lays out a number of models of inclusive agribusiness growth, grouped into three categories (i) institutional arrangements for improving productivity of smallholders operating in spot markets, (ii) various types of contract farming arrangements, and (iii) large-scale farms that generate jobs and/or include community equity shares. The institutional and policy context as well as commodity characteristics that favor these models are discussed within a simple transactions cost framework. Examples of apparent successes with each of these models are provided, many based on direct interviews and case studies of innovative firms.

The final section discusses cross-cutting policy priorities to enable the growth of commercial agriculture and agribusiness. These include continuing reforms to liberalize product and input markets, access to technology and skills, stimulating financial and risk markets, securing land rights, and investment in infrastructure through public-private partnerships. Priorities differ by value chain and implementation presents challenges of delicately balancing state intervention and leadership with private initiative. These challenges are illustrated through examples from Africa as well as emerging countries of Asia and Africa.

Evaluating the contribution of weather and its individual components to recent yield trends can be useful to predict the response of crop production to future climate change, but different modeling approaches can yield diverging results. We used two common approaches to evaluate the effect of weather trends on maize (Zea mays L.) and wheat (Triticum aestivum L.) production in 12 U.S. counties, and investigate sources of disparities between the two methods. We first used the Decision Support System for Agrotechnology Transfer (DSSAT) model from 1984 to 2008 to evaluate the contribution of weather changes to simulated yield trends in six counties for each crop, each county being located in one of the top 10 U.S. producing states for that crop. A parallel analysis was conducted by multiplying inter-annual weather sensitivity of county-level yields with observed weather trends to estimate weather contributions to empirical yield trends. Weather had a low (maize) to high (wheat) contribution to simulated yield trends, with rain having the largest effect. In contrast, weather and rain had lower contributions to empirical yield trends. Along with evidence from previous studies, this suggests that DSSAT may be too sensitive to water thus inflating the importance of rain. Moreover, the time period used to compute yield trends also had a large effect on the importance of weather and its individual components. Our results highlight the importance of using multiple computation approaches and different time periods when estimating weather-related yield trends.

Genetic improvements in heat tolerance of wheat provide a potential adaptation response to long-term warming trends, and may also boost yields in wheat-growing areas already subject to heat stress. Yet there have been few assessments of recent progress in breeding wheat for hot environments. Here, data from 25 years of wheat trials in 76 countries from the International Maize and Wheat Improvement Center (CIMMYT) are used to empirically model the response of wheat to environmental variation and assess the genetic gains over time in different environments and for different breeding strategies. Wheat yields exhibited the most sensitivity to warming during the grain-filling stage, typically the hottest part of the season. Sites with high vapour pressure deficit (VPD) exhibited a less negative response to temperatures during this period, probably associated with increased transpirational cooling. Genetic improvements were assessed by using the empirical model to correct observed yield growth for changes in environmental conditions and management over time. These ‘climate-corrected’ yield trends showed that most of the genetic gains in the high-yield-potential Elite Spring Wheat Yield Trial (ESWYT) were made at cooler temperatures, close to the physiological optimum, with no evidence for genetic gains at the hottest temperatures. In contrast, the Semi-Arid Wheat Yield Trial (SAWYT), a lower-yielding nursery targeted at maintaining yields under stressed conditions, showed the strongest genetic gains at the hottest temperatures. These results imply that targeted breeding efforts help us to ensure progress in building heat tolerance, and that intensified (and possibly new) approaches are needed to improve the yield potential of wheat in hot environments in order to maintain global food security in a warmer climate.

Wheat is a staple crop throughout much of India, but in many areas it is commonly sown past the optimum window for yields. Recent technologies, such as adoption of no-till practices or earlier maturing cotton and rice varieties, have enabled some farmers to sow wheat earlier, but repeatable and publicly available measurements of sow date trends are lacking. Here we utilize satellite measurements since 2000 to estimate sow dates over a decade throughout wheat growing areas in India. Comparisons with ground-based sow dates in Punjab confirmed the reliability of satellite estimates, and data from two independent satellite sensors were used as a robustness check. We find statistically significant (p < 0.05) shifts toward earlier sowing of wheat throughout much of Haryana and Uttar Pradesh, with insignificant changes in Punjab. A production-weighted average of the entire region indicates that, on average, wheat was sown 1 week earlier by 2010 than it was at the beginning of the decade. Using previously published experimental estimates of yield gains from earlier sowing, we estimate that an overall yield gain of at least 5% averaged across India can be explained by the sow date trend. Given that national yield changes since 2000 have been less than 5%, our results indicate that the sow date shift has been a major factor in yield changes over the past decade, and that the net yield effect of all factors other than sow date has been close to zero, perhaps even negative. The results also indicate that sow dates in much of Haryana and western Uttar Pradesh are nearing or already at the optimum window for yields, so that yield benefits from sow date shifts will likely diminish in the next decade.