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Forage management and environmental conditions affect water soluble carbohydrate (WSC) storage, and, in turn, influence ruminant forage utilization in silvopastoral systems. The objective was to determine effects of four dependent variables: forage species [(non-native, C3 (orchardgrass (Dactylis glomerata L.)) and native C4 mix (8:1:1 big bluestem (Andropogon gerardii Vitman), little bluestem (Schizachyrium scoparium Michx. Nash) and indiangrass (Sorghastrum nutans L.))], fertility (poultry litter and an unfertilized control), forage sampling date (mid-May, late-May, early-June, mid-June, and late-June), and hour of day (0800, 1100, 1400, and 1700 h) on WSC accumulation in a silvopasture. Concentrations of WSC (g kg DM−1) were greater (p ≤ 0.05) for C3 forages, with poultry litter not impacting WSC accumulation. Overall, WSC was greatest in mid-June, with the lowest WSC concentration observed at 0800 compared to 1100, 1400, and 1700 h (p ≤ 0.05). Therefore, harvesting forages later in the day resulted in greater WSC. A stepwise regression model indicated acid detergent fiber, ash, and forage P concentration were the best predictors (R2 = 0.85, p ≤ 0.05) of forage WSC. These results may be useful in future studies aimed at explaining diurnal cattle grazing preference and optimum forage harvest timing in silvopastoral systems.

The physiographic region of the Central Russian Upland, situated in the Central part of Eastern Europe, is characterized by very fertile grassland soils—Chernozems (Mollisols in the USDA taxonomy). However, over the last several centuries this region has experienced intense land-use conversion. The most widespread and significant land-use change is the extensive cultivation of these soils. As a result, Chernozems of the region that were some of the most naturally fertile soils in the world with thick A horizons had become, by the second half of the 19th century, weakly productive, with decreased stocks of organic matter. When not protected by plant cover, water and wind erosion degraded the open fields. The investigation of methods for rehabilitation and restoration of Chernozems resulted in the practice of afforestation of agricultural lands (mainly by windbreak planting). Preferences of agroforestry practices were initially connected with protection of cropland from wind and water erosion, improvement of microclimate for crop growth, and providing new refugia for wild animal and plant habitats. During the last several decades, tree windbreaks have begun to be viewed as ecosystems with great potential for atmospheric carbon sequestration, which plays a positive role in climate change mitigation. For the evaluation of windbreak influence on Chernozem soils, a study was developed with three field study areas across a climatic gradient from cool and wet in the north of the region to warm and dry in the south. Windbreak age ranged from 55–57 years. At each site, soil pits were prepared within the windbreak, the adjacent crop fields of 150 years of cultivation, and nearby undisturbed grassland. Profile descriptions were completed to a depth of 1.5 m. A linear relationship was detected between the difference in organic-rich surface layer (A + AB horizon) thickness of soils beneath windbreaks and undisturbed grasslands and a climate index, the hydrothermal coefficient (HTC). These results indicate that windbreaks under relatively cooler and wetter climate conditions are more favorable for organic matter accumulation in the surface soil. For the 0–100 cm layer of the Chernozems beneath windbreaks, an increase in organic C stocks comparable with undisturbed grassland soils (15–63 Mg·ha−1) was detected. Significant growth of soil organic matter stocks was identified not only for the upper 30 cm, but also for the deeper layer (30–100 cm) of afforested Chernozems. These findings illustrate that, in the central part of Eastern Europe, tree windbreaks improve soil quality by enhancing soil organic matter while providing a sink for atmospheric carbon in tree biomass and soil organic matter.

Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality.