Seasonal Changes of Soil Quality Indicators in Selected Arid Cropping Systems

Improving the soil quality in arid agro-ecosystems requires a greater understanding of how the time-of-sampling and management affect the soil measurements. We evaluated the selected soil quality indicators on samples collected at a 0–0.15 m depth, and at various sampling dates of the year, corresponding to the fall of 2015, winter of 2015/2016, spring of 2016, and the summer of 2016. The three crop management systems sampled included alfalfa (Medicago sativa), upland cotton (Gossypium hirsutum), and pecan (Carya illinoinensis). The soil properties measured included the wet aggregate stability (WAS), mean weight diameter of dry aggregates (MWD), dry aggregates greater than 2 mm (AGG >2 mm), dry aggregates less than 0.25 mm (AGG <0.25 mm), available water capacity (AWC), soil organic matter (SOM), permanganate oxidizable carbon (POXC), soil bulk density (BD), soil electrical conductivity (EC), pH, nitrate-nitrogen (NO3-N), extractable potassium (K), extractable phosphorus (P), calcium (Ca), magnesium (Mg), sodium adsorption ratio (SAR), and micronutrients (zinc, iron, copper, and manganese). Out of the 21 soil measurements, 15 varied significantly with the time-of-sampling within a year, although there were no consistent trends in variability. However, only a few measurements differed significantly with the crop management practices tested. Wet aggregate stability, MWD, AWC, and BD were significantly higher in the summer, while POXC and SOM were significantly higher in the fall and winter, respectively. Soil quality indicators such as NO3-N, K, and P decreased significantly during the spring. This study shows that the seasonal variability of the soil measurements can be significant in the arid agro-ecosystems, with the magnitude of variation depending on the measurement type. The soil managers in the region need to account for this variability, in order to be able to assess the changes in the soil quality. Also, because of the variability that can occur across the different sampling dates within a year, it is advisable to sample during the same period every year, for a consistent interpretation of the directional changes of the soil quality indicators

Grass Buffer Strips Improve Soil Health and Mitigate Greenhouse Gas Emissions in Center-Pivot Irrigated Cropping Systems

Declining water resources and soil degradation have significantly affected agricultural sustainability across the world. In the southern High Plains of USA, buffer strips of perennial grasses alternating with cultivated corn strips were introduced in center-pivot irrigated crop fields to increase agronomic production and ecosystem services. A study was conducted to evaluate soil carbon (C) and nitrogen (N) dynamics, greenhouse gas (GHG) emissions, and soil health benefits of integrating circular grass buffer strips in the center-pivot irrigated corn production system. Multiple parameters were assessed in the grass buffer strips, and at distances of 1.52, 4.57, and 9.14 m away from the edges of grass strips in corn strips. While grasses in the buffer strips depleted N compared to corn strips, potential C mineralization (PCM) was 52.5% to 99.9% more in grass strips than in corn strips. Soil microbial biomass C (MBC) content was 36.7% to 52.5% greater in grass strips than in corn strips. Grass buffer also reduced carbon dioxide (CO2) and nitrous oxide (N2O) emissions from corn strips. Grass buffer strips can improve soil health and sustainability in center-pivot irrigated cropping systems by increasing soil C components and reducing GHG emissions

Visible Near-Infrared Reflectance and Laser-Induced Breakdown Spectroscopy for Estimating Soil Quality in Arid and Semiarid Agroecosystems

Visible near-infrared reflectance spectroscopy (VNIRS) and laser-induced breakdown spectroscopy (LIBS) are potential methods for the rapid and less expensive assessment of soil quality indicators (SQIs). The specific objective of this study was to compare VNIRS and LIBS for assessing SQIs. Data was collected from over 140 soil samples taken from multiple agricultural management systems in New Mexico, belonging to arid and semiarid agroecosystems. Sampled sites included New Mexico State University Agricultural Science Center research fields and several commercial farm fields in New Mexico. Partial least squares regression (PLSR) was used to establish predictive relationships between spectral data and SQIs. Fifteen soil measurements were modeled including the soil organic matter (SOM), permanganate oxidizable carbon (POXC), total microbial biomass (TMB), total bacteria biomass (TBB), total fungi biomass (TFB), mean weight diameter of dry aggregates (MWD), aggregates 2–4 mm (AGG > 2 mm), aggregates < 0.25 mm (AGG < 0.25 mm), wet aggregate stability (WAS), electrical conductivity (EC), calcium (Ca), magnesium (Mg), sodium (Na), and iron (Fe). Overall, calibrations based on measurements irrespective of locations performed better for LIBS and combined VNIRS-LIBS. Measurements separated according to locations highly improved the quality of prediction for VNIRS as compared to combined locations. For example, the prediction R2 values for regression of VNIRS were 0.19 for SOM, 0.30 for POXC, 0.24 for MWD, 0.15 for AGG > 2 mm, and 0.13 for EC in combined datasets irrespective of location. When separated according to locations, for one of the locations, the predictive R2 values for VNIRS were 0.48 for SOM, 0.70 for POXC, 0.67 for MWD, 0.60 for AGG > 2 mm, and 0.51 for EC. The prediction values varied with the sampling time for both LIBS and VNIRS. For example, the prediction values of some SQIs using VNIRS were higher in samples collected in winter for measurements, including SOM (0.90), MWD (0.96), WAS (0.66), and EC (0.94). Using the VNIRS, the corresponding predictive values for the same SQIs were lower for samples collected in the fall (SOM (0.61), MWD (0.45), WAS (0.46), and EC (0.65)). While this study illustrates the prospects of VNIRS and LIBS for estimating SQIs, a more comprehensive evaluation, using a larger regional dataset, is required to understand how the site and soil factors affect VNIRS and LIBS, in order to enhance the utility of these methods for soil quality assessment in arid and semiarid agroecosystems

Crop Vulnerability to Weather and Climate Risk: Analysis of Interacting Systems and Adaptation Efficacy for Sustainable Crop Production

Climate change is increasing mean and extreme temperatures in the Southwestern United States, leading to a suite of changes affecting agricultural production. These include changes in water, soils, pathogens, weeds, and pests comprising the production environment. The aim of this synthesis is to describe the anticipated leading agricultural pressures and adaptive responses, many of which are near-term actions with longer-term consequences. In the semiarid Southwestern United States, climate change is expected to increase water scarcity. Surface water shortage is the leading reason for recent diminished crop yields in the Southwest. Drought and lack of water represent the leading regional weather-related cause of crop loss from 1989 to 2017. Thus, water scarcity has been and will continue to be a critical factor leading to regional crop vulnerability. Soils, pathogens, weeds, and insects are components of the agricultural production environment and are directly influenced by near-term weather and long-term climate conditions. Field crops, vegetable crops, and perennial crops have unique production requirements and diverse management options, many already used in farm management, to cope with production environment changes to build climate resilience. Farmers and ranchers continuously respond to changing conditions on a near-term basis. Long-term planning and novel adaptation measures implemented may now build nimble and responsive systems and communities able to cope with future conditions. While decision-support tools and resources are providing increasingly sophisticated approaches to cope with production in the 21st century, we strive to keep pace with the cascading barrage of inter-connected agricultural challenges