Global reductions in manual agricultural work capacity due to climate change.

Manual outdoor work is essential in many agricultural systems. Climate change will make such work more stressful in many regions due to heat exposure. The physical work capacity metric (PWC) is a physiologically based approach that estimates an individual's work capacity relative to an environment without any heat stress. We computed PWC under recent past and potential future climate conditions. Daily values were computed from five earth system models for three emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) and three time periods: 1991-2010 (recent past), 2041-2060 (mid-century) and 2081-2100 (end-century). Average daily PWC values were aggregated for the entire year, the growing season, and the warmest 90-day period of the year. Under recent past climate conditions, the growing season PWC was below 0.86 (86% of full work capacity) on half the current global cropland. With end-century/SSP5-8.5 thermal conditions this value was reduced to 0.7, with most affected crop-growing regions in Southeast and South Asia, West and Central Africa, and northern South America. Average growing season PWC could falls below 0.4 in some important food production regions such as the Indo-Gangetic plains in Pakistan and India. End-century PWC reductions were substantially greater than mid-century reductions. This paper assesses two potential adaptions-reducing direct solar radiation impacts with shade or working at night and reducing the need for hard physical labor with increased mechanization. Removing the effect of direct solar radiation impacts improved PWC values by 0.05 to 0.10 in the hottest periods and regions. Adding mechanization to increase horsepower (HP) per hectare to levels similar to those in some higher income countries would require a 22% increase in global HP availability with Sub-Saharan Africa needing the most. There may be scope for shifting to less labor-intensive crops or those with labor peaks in cooler periods or shift work to early morning.

Cropland expansion in Ecuador between 2000 and 2016.

We describe changes in the cropland distribution for physiographic and bioregions of continental Ecuador between 2000 and 2016 using Landsat satellite data and government statistics. The cloudy conditions in Ecuador are a major constraint to satellite data analysis. We developed a two-stage cloud filtering algorithm to create cloud-free multi-temporal Landsat composites that were used in a Random Forest model to identify cropland. The overall accuracy of the model was 78% for the Coast region, 86% for the Andes, and 98% for the Amazon region. Cropland density was highest in the coastal lowlands and in the Andes between 2500 and 4400 m. During this period, cropland expansion was most pronounced in the Páramo, Chocó Tropical Rainforests, and Western Montane bioregions. There was no cropland expansion detected in the Eastern Foothill forests bioregion. The satellite data analysis further showed a small contraction of cropland (4%) in the Coast physiographic region, and cropland expansion in the Andes region (15%), especially above 3500m, and in the Amazon region (57%) between 2000 and 2016. The government data showed a similar contraction for the Coast (7%) but, in contrast with the satellite data, they showed a large agricultural contraction in the Andes (39%) and Amazon (50%). While the satellite data may be better at estimating relative change (trends), the government data may provide more accurate absolute numbers in some regions, especially the Amazon because separating pasture and tree crops from forest with satellite data is challenging. These discrepancies illustrate the need for careful evaluation and comparison of data from different sources when analyzing land use change.

Estimating lime requirements for tropical soils: Model comparison and development.

Acid tropical soils may become more productive when treated with agricultural lime, but optimal lime rates have yet to be determined in many tropical regions. In these regions, lime rates can be estimated with lime requirement models based on widely available soil data. We reviewed seven of these models and introduced a new model (LiTAS). We evaluated the models' ability to predict the amount of lime needed to reach a target change in soil chemical properties with data from four soil incubation studies covering 31 soil types. Two foundational models, one targeting acidity saturation and the other targeting base saturation, were more accurate than the five models that were derived from them, while the LiTAS model was the most accurate. The models were used to estimate lime requirements for 303 African soil samples. We found large differences in the estimated lime rates depending on the target soil chemical property of the model. Therefore, an important first step in formulating liming recommendations is to clearly identify the soil property of interest and the target value that needs to be reached. While the LiTAS model can be useful for strategic research, more information on acidity-related problems other than aluminum toxicity is needed to comprehensively assess the benefits of liming.

Increased women's empowerment and regional inequality in Sub-Saharan Africa between 1995 and 2015.

Women's empowerment is a fundamental human right but attempts to measure progress in this area have been limited. We used 142 nationally representative surveys to quantify empowerment in six domains (Intimate Partner Violence, Family Planning, Reproductive Healthcare, Employment, Education, and Decision-Making) for first-level subdivisions of all countries in Sub-Saharan Africa for three years (1995, 2005, and 2015). The possible value for each domain ranged between zero (worst) and one (best). The median value for employment decreased by 0.02, but it increased between 0.09 and 0.16 for the other domains. The average empowerment score increased from 0.44 to 0.53, but it remained low for Education (0.34). While progress was clear and consistent, it was uneven within and between countries, and Sahelian West Africa fell further behind. The expanded understanding of geographic variation and trends in women's empowerment that we provide should be instrumental in efforts to improve women's lives.

Evaluating the quality of remote sensing products for agricultural index insurance.

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

Fertilizer and grain prices constrain food production in sub-Saharan Africa.

Crop yields across sub-Saharan Africa are much lower than what is attainable given the environmental conditions and available technologies. Closing this 'ecological yield gap' is considered an important food security and rural welfare goal. It is not clear, however, whether it is economically sensible for farmers to substantially increase crop yields. Here we estimate the local yield response of maize to fertilizer across sub-Saharan Africa with an empirical machine-learning model based on 12,081 trial observations and with a mechanistic model. We show that the average 'economic yield gap'-the difference between current yield and profit-maximizing yield-is about one-quarter of the ecological yield gap. Furthermore, although maize yields could be profitably doubled, the economic incentives to do so may be weak. Our findings suggest that agricultural intensification in sub-Saharan Africa could be supported by complementary agronomic approaches to improve soil fertility, lowering the fertilizer cost, and by spatial targeting of fertilizer recommendations.

Seasonal, annual, and spatial variation in cereal prices in Sub-Saharan Africa.

Local food prices are key indicators of food security and market conditions. Yet price data are often not available, particularly for rural areas of Sub-Saharan Africa. We compiled data from 168 markets to study spatial and temporal price variation. We found that prices slightly increase when the preceding growing season was dry. Across the continent, there is pronounced seasonal variation, with lowest prices 2-3 months after harvest and highest prices just before harvest. A predictive model explained 42% of the spatial variation in prices. Our results show that spatial and temporal price variation can be generalized and that prices can be estimated for unsampled locations or months. Such estimates may be used to improve the targeting of food security interventions and strengthen empirical policy-oriented research.

The scale dependency of spatial crop species diversity and its relation to temporal diversity.

Increasing crop species diversity can enhance agricultural sustainability, but the scale dependency of the processes that shape diversity and of the effects of diversity on agroecosystems is insufficiently understood. We used 30 m spatial resolution crop classification data for the conterminous United States to analyze spatial and temporal crop species diversity and their relationship. We found that the US average temporal (crop rotation) diversity is 2.1 effective number of species and that a crop's average temporal diversity is lowest for common crops. Spatial diversity monotonically increases with the size of the unit of observation, and it is most strongly associated with temporal diversity when measured for areas of 100 to 400 ha, which is the typical US farm size. The association between diversity in space and time weakens as data are aggregated over larger areas because of the increasing diversity among farms, but at intermediate aggregation levels (counties) it is possible to estimate temporal diversity and farm-scale spatial diversity from aggregated spatial crop diversity data if the effect of beta diversity is considered. For larger areas, the diversity among farms is usually much greater than the diversity within them, and this needs to be considered when analyzing large-area crop diversity data. US agriculture is dominated by a few major annual crops (maize, soybean, wheat) that are mostly grown on fields with a very low temporal diversity. To increase crop species diversity, currently minor crops would have to increase in area at the expense of these major crops.

The Female Empowerment Index (FEMI): spatial and temporal variation in women's empowerment in Nigeria.

Improving female empowerment is an important human rights and development goal that needs better monitoring. A number of indices have been developed to track female empowerment at the national level, but these are incomplete and may obscure important sub-national variation. We developed the Female Empowerment Index (FEMI) to track multiple domains of women's empowerment at the sub-national level. The index is based on six categories of empowerment: violence against women, employment, education, reproductive healthcare, decision making, and access to contraceptives. The FEMI has a range of zero to one (low to high empowerment), and it is calculated as the mean proportion of positive outcomes in the six categories. To provide a proof of concept, we computed the FEMI for Nigeria and its 36 states from five Demographic and Health Surveys between the years of 1990 and 2013, using questions asked to 98,542 women between 15 and 49 years old. At the national level, the FEMI increased from 0.34 to 0.48. However, there was substantial sub-national variation, with state-level values ranging from 0.16-0.60 in 1990 to 0.19-0.73 in 2013. Our findings thus illustrate the importance of considering sub-national variation in female empowerment. The FEMI can be readily computed for other countries, and its ability to track spatial and temporal variation in woman's empowerment across a broad set of categories may make it more useful than existing approaches.

The environmental consequences of climate-driven agricultural frontiers.

Growing conditions for crops such as coffee and wine grapes are shifting to track climate change. Research on these crop responses has focused principally on impacts to food production impacts, but evidence is emerging that they may have serious environmental consequences as well. Recent research has documented potential environmental impacts of shifting cropping patterns, including impacts on water, wildlife, pollinator interaction, carbon storage and nature conservation, on national to global scales. Multiple crops will be moving in response to shifting climatic suitability, and the cumulative environmental effects of these multi-crop shifts at global scales is not known. Here we model for the first time multiple major global commodity crop suitability changes due to climate change, to estimate the impacts of new crop suitability on water, biodiversity and carbon storage. Areas that become newly suitable for one or more crops are Climate-driven Agricultural Frontiers. These frontiers cover an area equivalent to over 30% of the current agricultural land on the planet and have major potential impacts on biodiversity in tropical mountains, on water resources downstream and on carbon storage in high latitude lands. Frontier soils contain up to 177 Gt of C, which might be subject to release, which is the equivalent of over a century of current United States CO2 emissions. Watersheds serving over 1.8 billion people would be impacted by the cultivation of the climate-driven frontiers. Frontiers intersect 19 global biodiversity hotspots and the habitat of 20% of all global restricted range birds. Sound planning and management of climate-driven agricultural frontiers can therefore help reduce globally significant impacts on people, ecosystems and the climate system.

Spatial variation in fertilizer prices in Sub-Saharan Africa.

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

Agricultural intensification was associated with crop diversification in India (1947-2014).

Declines in agricultural biodiversity associated with modern farming practices may negatively affect the sustainability of agro-ecosystems, but formal knowledge of historical variation in spatio-temporal variation of agro-biodiversity is limited. We used time series of national (1947-2014) and district-level (1956-2008) crop distribution data for India to show that despite strong agricultural intensification after 1960, the average crop species diversity at the district level was stable, but increased at the country-level. While there was a decline in diversity in the major rice and wheat producing regions of northwestern India, associated with intensification of the production of these crops, diversity in western and southern India increased due to expansion of oilseeds and horticultural crops that replaced millet and sorghum. These opposite, but related, trends in crop-level diversity at the sub-national level partially canceled each other out at national level, but there nevertheless was a noticeable increase in overall crop diversity in India during this time period. Our results illustrate how patterns of change in crop diversity need to be considered at different levels of aggregation, and how a decrease in diversity associated with intensification and specialization in one area, may be associated with increased diversity elsewhere, and that support for intensive agriculture with relatively low crop diversity in some regions may be associated with an increase in crop diversity in other regions and at a higher level of aggregation.

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models.

The coexistence of different types of poultry operations such as free range and backyard flocks, large commercial indoor farms and live bird markets, as well as the presence of many areas where wild and domestic birds co-exist, make California susceptible to avian influenza outbreaks. The 2014-2015 highly pathogenic Avian Influenza (HPAI) outbreaks affecting California and other states in the United States have underscored the need for solutions to protect the US poultry industry against this devastating disease. We applied disease distribution models to predict where Avian influenza is likely to occur and the risk for HPAI outbreaks is highest. We used observations on the presence of Low Pathogenic Avian influenza virus (LPAI) in waterfowl or water samples at 355 locations throughout the state and environmental variables relevant to the disease epidemiology. We used two algorithms, Random Forest and MaxEnt, and two data-sets Presence-Background and Presence-Absence data. The models performed well (AUCc > 0.7 for testing data), particularly those using Presence-Background data (AUCc > 0.85). Spatial predictions were similar between algorithms, but there were large differences between the predictions with Presence-Absence and Presence-Background data. Overall, predictors that contributed most to the models included land cover, distance to coast, and broiler farm density. Models successfully identified several counties as high-to-intermediate risk out of the 8 counties with observed outbreaks during the 2014-2015 HPAI epizootics. This study provides further insights into the spatial epidemiology of AI in California, and the high spatial resolution maps may be useful to guide risk-based surveillance and outreach efforts.

RiceAtlas, a spatial database of global rice calendars and production.

Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global crop calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.

Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes.

Understanding dietary patterns is vital to reducing the number of people experiencing hunger (about 795 million), micronutrient deficiencies (2 billion), and overweight or obesity (2.1 billion). We characterize global trends in dietary quality by estimating micronutrient density of the food supply, prevalence of inadequate intake of 14 micronutrients, and average prevalence of inadequate intake of these micronutrients for all countries between 1961 and 2011. Over this 50-year period, the estimated prevalence of inadequate intakes of micronutrients has declined in all regions due to increased total production of food and/or micronutrient density. This decline has been particularly strong in East and Southeast Asia and weaker in South Asia and sub-Saharan Africa. Sub-Saharan Africa is the only region where dietary micronutrient density has declined over this 50-year period. At the global level, micronutrients with the lowest levels of adequate estimated intake are calcium, iron, vitamin A, and zinc, but there are strong differences between countries and regions. Fortification has reduced the estimated prevalence of inadequate micronutrient intakes in all low-income regions, except South Asia. The food supply in many countries is still far below energy requirements, which suggests a need to increase the availability and accessibility of nutritious foods. Countries where the food energy supply is adequate show a very large variation in dietary quality, and in many of these countries people would benefit from more diverse diets with a greater proportion of micronutrient-dense foods. Dietary quality can be improved through fortification, biofortification, and agricultural diversification, as well as efforts to improve access to and use of micronutrient-dense foods and nutritional knowledge. Reducing poverty and increasing education, especially of women, are integral to sustainably addressing malnutrition.

Point stresses during reproductive stage rather than warming seasonal temperature determine yield in temperate rice.

Climate change is predicted to shift temperature regimes in most agricultural areas with temperature changes expected to impact yields of most crops, including rice. These temperature-driven effects can be classified into point stresses, where a temperature event during a sensitive stage drives a reduction in yield, or seasonal warming losses, where raised temperature is thought to increase maintenance energy demands and thereby decrease available resources for yield formation. Simultaneous estimation of the magnitude of each temperature effect on yield has not been well documented due to the inherent difficulty in separating their effects. We simultaneously quantified the magnitude of each effect for a temperate rice production system using a large data set covering multiple locations with data collected from 1995 to 2015, combined with a unique probability-based modeling approach. Point stresses, primarily cold stress during the reproductive stages (booting and flowering), were found to have the largest impact on yield (over 3 Mg/ha estimated yield losses). Contrary to previous reports, yield losses caused by increased temperatures, both seasonal and during grain-filling, were found to be small (approximately 1-2% loss per °C). Occurrences of cool temperature events during reproductive stages were found to be persistent over the study period, and within season, the likelihood of a cool temperature event increased when flowering occurred later in the season. Short and medium grain types, typically recommended for cool regions, were found to be more tolerant of cool temperatures but more sensitive to heat compared to long grain cultivars. These results suggest that for temperate rice systems, the occurrence of periodic stress events may currently overshadow the impacts of general warming temperature on crop production.

The Use of Spatial and Spatiotemporal Modeling for Surveillance of H5N1 Highly Pathogenic Avian Influenza in Poultry in the Middle East.

Since 2005, H5N1 highly pathogenic avian influenza virus (HPAIV) has severely impacted the economy and public health in the Middle East (ME) with Egypt as the most affected country. Understanding the high-risk areas and spatiotemporal distribution of the H5N1 HPAIV in poultry is prerequisite for establishing risk-based surveillance activities at a regional level in the ME. Here, we aimed to predict the geographic range of H5N1 HPAIV outbreaks in poultry in the ME using a set of environmental variables and to investigate the spatiotemporal clustering of outbreaks in the region. Data from the ME for the period 2005-14 were analyzed using maximum entropy ecological niche modeling and the permutation model of the scan statistics. The predicted range of high-risk areas (P > 0.60) for H5N1 HPAIV in poultry included parts of the ME northeastern countries, whereas the Egyptian Nile delta and valley were estimated to be the most suitable locations for occurrence of H5N1 HPAIV outbreaks. The most important environmental predictor that contributed to risk for H5N1 HPAIV was the precipitation of the warmest quarter (47.2%), followed by the type of global livestock production system (18.1%). Most significant spatiotemporal clusters (P < 0.001) were detected in Egypt, Turkey, Kuwait, Saudi Arabia, and Sudan. Results suggest that more information related to poultry holding demographics is needed to further improve prediction of risk for H5N1 HPAIV in the ME, whereas the methodology presented here may be useful in guiding the design of surveillance programs and in identifying areas in which underreporting may have occurred.

Historical distribution of Sundaland's Dipterocarp rainforests at Quaternary glacial maxima.

The extent of Dipterocarp rainforests on the emergent Sundaland landmass in Southeast Asia during Quaternary glaciations remains a key question. A better understanding of the biogeographic history of Sundaland could help explain current patterns of biodiversity and support the development of effective forest conservation strategies. Dipterocarpaceae trees dominate the rainforests of Sundaland, and their distributions serve as a proxy for rainforest extent. We used species distribution models (SDMs) of 317 Dipterocarp species to estimate the geographic extent of appropriate climatic conditions for rainforest on Sundaland at the last glacial maximum (LGM). The SDMs suggest that the climate of central Sundaland at the LGM was suitable to sustain Dipterocarp rainforest, and that the presence of a previously suggested transequatorial savannah corridor at that time is unlikely. Our findings are supported by palynologic evidence, dynamic vegetation models, extant mammal and termite communities, vascular plant fatty acid stable isotopic compositions, and stable carbon isotopic compositions of cave guano profiles. Although Dipterocarp species richness was generally lower at the LGM, areas of high species richness were mostly found off the current islands and on the emergent Sunda Shelf, indicating substantial species migration and mixing during the transitions between the Quaternary glacial maxima and warm periods such as the present.

Multiple lines of evidence for the origin of domesticated chili pepper, Capsicum annuum, in Mexico.

The study of crop origins has traditionally involved identifying geographic areas of high morphological diversity, sampling populations of wild progenitor species, and the archaeological retrieval of macroremains. Recent investigations have added identification of plant microremains (phytoliths, pollen, and starch grains), biochemical and molecular genetic approaches, and dating through (14)C accelerator mass spectrometry. We investigate the origin of domesticated chili pepper, Capsicum annuum, by combining two approaches, species distribution modeling and paleobiolinguistics, with microsatellite genetic data and archaeobotanical data. The combination of these four lines of evidence yields consensus models indicating that domestication of C. annuum could have occurred in one or both of two areas of Mexico: northeastern Mexico and central-east Mexico. Genetic evidence shows more support for the more northern location, but jointly all four lines of evidence support central-east Mexico, where preceramic macroremains of chili pepper have been recovered in the Valley of Tehuacán. Located just to the east of this valley is the center of phylogenetic diversity of Proto-Otomanguean, a language spoken in mid-Holocene times and the oldest protolanguage for which a word for chili pepper reconstructs based on historical linguistics. For many crops, especially those that do not have a strong archaeobotanical record or phylogeographic pattern, it is difficult to precisely identify the time and place of their origin. Our results for chili pepper show that expressing all data in similar distance terms allows for combining contrasting lines of evidence and locating the region(s) where cultivation and domestication of a crop began.