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.

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.

There is a need to quantify agriculture’s potential to sequester carbon (C) to inform global approaches aimed at mitigating climate change effects. Many factors including climate, crop, soil management practices, and soil type can influence the contribution of agriculture to the global carbon cycle. The objective of this study was to investigate the C sequestration potential of conservation agriculture (CA) (defined by minimal soil disturbance, maintaining permanent soil cover, and crop rotations). This study used micrometeorological methods to measure carbon dioxide (CO₂) flux from several alternative CA practices in Harare, central Zimbabwe. Micrometeorological methods can detect differences in total CO₂ emissions of agricultural management practices; our results show that CA practices produce less CO₂ emissions. Over three years of measurement, the mean and standard error (SE) of CO₂ emissions for the plot with the most consistent CA practices was 0.564 ± 0.0122 g CO₂ m⁻² h⁻¹, significantly less than 0.928 ± 0.00859 g CO₂ m⁻² h⁻¹ for the conventional tillage practice. Overall CA practices of no-till with the use of cover crops produced fewer CO₂ emissions than conventional tillage and fallow.

Tree species and fertilization sources may affect the soil organic carbon (SOC) and fertility in agroforestry (AF) systems in temperate regions, but this remains unexplored. This study assesses the longitudinal trends in the SOC and major nutrients in a 17-year AF site established on an Ultisol in Fayetteville, AR. We evaluated how management and fertilization (mineral vs. organic) affect the soil’s physical and chemical properties using the tree species: red oak (Quercus rubra L.) and pecan Carya illinoinensis (Wangenh.) K. Koch. Long-term applications of poultry litter increased the soil pH, whereas the Mg and S concentrations decreased compared to the baseline levels (1999). Poultry litter increased the soil P concentration in 53% compared to the baseline levels and reduced the soil C:N ratio. After 17 years, increased Ca, SOC, and N concentrations were observed in the soils under the pecan stands that received poultry litter. Positive correlations occurred between the SOC and N (r = 0.96) as well as the Ca (r = 0.71) and P (r = 0.61) at a soil depth of 0–15 cm. Our findings demonstrated that the changes in SOC and fertility in the AF soils vary according to tree species and fertilization, mostly due to distinctive leaf litter and nutrient inputs. Organic matter accumulation increased the SOC retention and nutrient availability in the temperate AF systems.

Grass root production and decomposition is a major source of C entering soils, although rates are largely unknown based on edaphic and management factors. Therefore, study objectives were to evaluate four explanatory variables including forage species (native and nonnative), fertility (poultry litter and a control), soil moisture (udic and aquic), and pasture management (grazed and an ungrazed control) in order to evaluate driving factors for root turnover and subsequent soil organic matter formation in silvopastoral systems using the root litter bag technique. Native grass root decomposition was accelerated relative to the nonnative forage based on root mass balance, as well as the exponential decay function, likely owing to greater five‐ and six‐C sugars and more digestible root tissues of native grasses. These physiochemical results suggest more favorable microbial food sources, which culminate in faster decomposition and greater microbially derived organic matter. Overall, there was greater root sloughing and subsequent soil organic matter formation potential with native grass species and poultry litter applications, with soil moisture affecting decomposition to a lesser extent. This study contributes to the understanding of complex interactions of grass species, soil moisture, nutrients, and grazing, which controls primary productivity, as well as nutrient cycling and C sequestration in silvopastures.