Minimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Systems-level studies aimed at determining how soil properties are linked to plant production and ultimately animal response spatially are lacking. This study aims to identify if grazing pressure is linked to soil properties, terrain attributes, and above-ground plant accumulation and nutritive value in a silvopastoral (or integrated tree-livestock) system. Overall, cattle prefer grazing native grasses (2.81 vs. 1.24 h ha −1 AU −1 ) and udic (dry) landscape positions compared to aquic (wet) areas (2.07 vs. 1.60 h ha −1 AU −1 ). Greater grazing frequency occurs in udic soils with greater phosphorus and potassium contents and with accumulated forage with less lignin ( P ≤ 0.05), which correspond to reduced elevation and greater tree height and diameter (shade) during summer mob grazing. Combining spatial monitoring technologies (both soil and animal) with forage allowance can optimize grazing systems management and sustainability spatially and temporally.

Soil microorganisms play crucial roles in nutrient cycling and provisioning ecosystem services. However, little is known about how soil microbial communities are affected by soil management and landscape position in silvopastures. The current study aimed to understand effects of forage species [non-native, cool season orchardgrass (Dactylis glomerata L.) and a warm-season native grass mix (Andropogon gerardii L. and Schizachyrium scoparium L.) planted in strips between hedgerows], soil fertility (poultry litter and a control), and soil moisture regimes (aquic and udic) on soil bacterial communities to evaluate linkages between terrain attributes and soil bacterial assemblages. Thirteen terrain attributes representing topographic variability were clustered into four topographic functional units (TFUs) using the k-means method, and their impact on soil microbial diversity was evaluated. Illumina sequencing results identified a soil moisture regime ? forage species interaction, with native grass species under wet (aquic) conditions resulting in the most diverse microbial assemblages relative to dry (udic) and wet soil conditions for the non-native forage (orchardgrass). These results suggest an enhanced soil microbial diversity under native grasses with greater available soil water. Overall, microbial diversity was negatively correlated with elevation, suggesting niche differentiation and microbial preference for lower elevations. Overall, TFUs and selected terrain attributes may be useful for predicting microbiota dynamics in integrated tree-livestock systems.

Abstract The Soil Management Assessment Framework (SMAF) is a quantitative soil quality (SQ) evaluation tool that is widely applied to assess soil response to specific agricultural management practices over time. Considering the reported SQ benefits of agroforestry (AF) systems and the potential usefulness of SMAF, the objective of this study was to evaluate the effects of tree species (pecan [Carya illinoinensis (Wangenh.) K. Koch] and northern red oak [Quercus rubra L.]), soil fertility source (poultry litter [PL] and inorganic N fertilizer [control]), and soil depth (0–15 and 15–30 cm) on SMAF‐derived SQ indices after 17 yr of management at an AF site in northwest Arkansas. Averaged across soil depth, soil organic C scores under red oak with PL application had a lower score (0.48) than red oak fertilized with inorganic N (0.60) and pecan receiving long‐term PL applications (0.60), which did not differ from pecan with inorganic N fertilizer application (0.51). Averaged across soil depth, the soil quality index (SQI) for pecan receiving PL applications was 1.1 times greater than that under red oak receiving PL and soils under pecan receiving inorganic N fertilizer. Soil quality assessments use in AF are novel, as SMAF has not been used to identify soil health in these systems, although specific tree crop codes need to be developed in SMAF. Results of this study demonstrate that soils planted under various tree species respond dissimilarly to fertilizer sources and that management may improve overall SQ.

Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( x ¯ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m−2 for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m−2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ−1 for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m−2 d−1, for corn and soybean, respectively, which is an improvement to the GPPMODIS product (2.44 and 3.30 g C m−2 d−1, respectively). Corn yield, calculated from GPPcalc (12.82 ± 0.65 Mg ha−1), corresponded well to official yield data (13.09 ± 0.09 Mg ha−1), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha−1). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.

Global agriculture is challenged to increase soil carbon sequestration and reduce greenhouse gas emissions while providing products for an increasing population. Growing crop production could be achieved through higher yield per hectare (i.e. intensive farming) or more hectares (extensive farming), which however, have different ecological and environmental consequences. Multiple lines of evidence indicate that expanding cropland for additional production may lead to loss of vegetation and soil carbon, and threaten the survival of wildlife. New concerns about the impacts of extensive farming have been raised for the US Corn Belt, one of the world’s most productive regions, as cropland has rapidly expanded northwestward unto grasslands and wetlands in recent years. Here we used a process-based ecosystem model to distinguish and quantify how natural drivers as well as intensive and extensive farming practices have altered grain production, soil carbon storage, and agricultural carbon footprint in the US Western Corn Belt since 1980. Compared to the period 1980–2005, we found that cropland expansion more than tripled in the most recent decade (2006–2016), becoming a significant factor contributing to growing grain production. Land use change in this period led to a soil carbon loss of 90.8 ± 14.7 Tg (1 Tg = 1012 g). As a result, grain production in this region shifted from carbon neutral to a carbon loss of 2.3 kg C kg−1 grain produced. The enlarging negative carbon footprint (ΔC/ΔP) indicates the major role that cropland expansion has had on the carbon cost of grain production in this region. Therefore, we should be more cautious to pursue high crop production through agricultural cropland conversion, particularly in those carbon-rich soils.

Core Ideas The crop water stress index was calculated for corn, soybean, and prairie using eddy covariance and canopy temperature. Crop water stress index increased with decreasing volumetric soil water content in tallgrass prairie with net ecosystem production sensitive to water deficits. Crop water stress index in corn and soybean increased at low and high volumetric soil water content demonstrating that carbon assimilation is affected by deficit and excess soil water contents. Corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] are predominantly produced in Iowa under rainfed conditions in which the amount and distribution of rainfall, and ambient air temperature, vary substantially among years. Prairie ecosystems also represent a small but significant portion of the landscape in the Midwest, but are subjected to the same variation in rainfall. Quantifying the effect of rainfall variation on ecosystem productivity is required to understand how the absence and the oversupply of water induces plant water stress and affects plant growth. We used the crop water stress index (CWSI) using canopy temperatures obtained from infrared temperature sensors coupled with eddy flux measurements to quantify the impact of water stress on corn, soybean, and prairie net ecosystem production (NEP) in central Iowa from June to August, 2006 through 2015. The relationships between CWSI and NEP, evapotranspiration (ET), and volumetric water content (VWC) were analyzed for these three canopies. Average seasonal CWSI values varied substantially among years and sites, indicating no‐stress and extreme water stress periods. The CWSI significantly increased with decreasing ET and NEP, signaling that water stress adversely affected transpiration and C assimilation. Prairie CWSI was linearly and negatively related to VWC. Corn and soybean CWSI increased with very dry and wet soil moisture regimes, indicating that corn–soybean cropping systems were negatively affected by both the absence and oversupply of water. The CWSI approach quantifies water stress in different agroecosystems to compare the responsiveness of these systems to the dynamics of seasonal rainfall patterns.

Afforestation trends were compared between two continentally-distinct, yet similar ecoregions to characterize similarities or differences in forest advancement due to natural and anthropogenic forcings. Temporal changes in forest cover were analyzed using high resolution aerial and satellite photographs for Southeast Iowa, USA, and satellite photographs for the western Belgorod Oblast, Russia. An increase in forested area was shown to occur over a 44-year period from 1970–2014 in Iowa where afforestation was reflected by the aggregation of smaller forest units. In the Belgorod region the opposite occurred in that there was an increase in the number of smaller forested units. The rate of forest expansion into open grassland areas, previously used as haying lands and pastures, was 14 m decade−1 and 8 m decade−1 in Iowa and the Belgorod Oblast, respectively. Based on current trends, predicted times for complete forest coverage in the study areas was estimated to be 80 years in Iowa and 300 years in the Belgorod Oblast. In both the Iowa and Belgorod Oblast, there was an increase in annual precipitation at the end of the 20th and the beginning of the 21st centuries, thus providing a contributing mechanism to forest advancement in the study regions and implications for future management practices.