With increasing affluence in many developing countries, the demand for livestock products is rising and the increasing feed requirement contributes to pressure on land resources for food and energy production. However, there is currently a knowledge gap in our ability to assess the extent and intensity of the utilization of land by livestock, which is the single largest land use in the world. We developed a spatial model that combines fine-scale livestock numbers with their associated energy requirements to distribute livestock grazing demand onto a map of energy supply, with the aim of estimating where and to what degree pasture is being utilized. We applied our model to Kazakhstan, which contains large grassland areas that historically have been used for extensive livestock production but for which the current extent, and thus the potential for increasing livestock production, is unknown. We measured the grazing demand of Kazakh livestock in 2015 at 286 Petajoules, which was 25% of the estimated maximum sustainable energy supply that is available to livestock for grazing. The model resulted in a grazed area of 1.22 million km2, or 48% of the area theoretically available for grazing in Kazakhstan, with most utilized land grazed at low intensities (average off-take rate was 13% of total biomass energy production). Under a conservative scenario, our estimations showed a production potential of 0.13 million tons of beef additional to 2015 production (31% increase), and much more with utilization of distant pastures. This model is an important step forward in evaluating pasture use and available land resources, and can be adapted at any spatial scale for any region in the world.

AimLarge and ecologically functioning steppe complexes have been lost historically across the globe, but recent land‐use changes may allow the reversal of this trend in some regions. We aimed to develop and map indicators of changing human influence using satellite imagery and historical maps, and to use these indicators to identify areas for broad‐scale steppe rewilding.LocationEurasian steppes of Kazakhstan.MethodsWe mapped decreasing human influence indicated by cropland abandonment, declining grazing pressure and rural outmigration in the steppes of northern Kazakhstan. We did this by processing ~5,500 Landsat scenes to map changes in cropland between 1990 and 2015, and by digitizing Soviet topographic maps and examining recent high‐resolution satellite imagery to assess the degree of abandonment of >2,000 settlements and >1,300 livestock stations. We combined this information into a human influence index (HI), mapped changes in HI to highlight where rewilding might take place and assessed how this affected the connectivity of steppe habitat.ResultsAcross our study area, about 6.2 million ha of cropland were abandoned (30.5%), 14% of all settlements were fully and 81% partly abandoned, and 76% of livestock stations were completely dismantled between 1990 and 2015, suggesting substantially decreasing human pressure across vast areas. This resulted in increased connectivity of steppe habitat.Main conclusionsThe steppes of Eurasia are experiencing massively declining human influence, suggesting large‐scale passive rewilding is taking place. Many of these areas are now important for the connectivity of the wider steppe landscape and can provide habitat for endangered megafauna such as the critically endangered saiga antelope. Yet, this window of opportunity may soon close, as recultivation of abandoned cropland is gaining momentum. Our aggregate human influence index captures key components of rewilding and can help to devise strategies for fostering large, connected networks of protected areas in the steppe.

Estimation of actual evapotranspiration (ET a ) based on remotely sensed imagery is very valuable in agricultural regions where ET a rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) to estimate late season, post-harvest ET a rates from fields with a cover crop planted after a cash crop (in this case, a rye/radish/pea mixture planted after spring wheat). Remotely sensed ET o F (unit-less fraction of grass-based reference ET, ET o ) maps were generated using Erdas Imagine software for a 260 km 2 area in northeastern South Dakota, USA. Meteorological information was obtained from a Bowen-Ratio Energy Balance System (BREBS) located within the image. Nine image dates were used for the growing season, from May through October. Five of those nine were captured during the cover crop season. METRIC was found to successfully differentiate between fields with and without cover crops. In a blind comparison, METRIC compared favorably with the estimated ET a rates found using the BREBS (ET λE ), with a difference in total estimated ET a for the cover crop season of 7%.

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