Abstract
In the Texas High Plains (THP), diminishing irrigation well-capacities, and increasing costs of energy and equipment associated with groundwater extraction and application are contributing factors to a transition from irrigated to dryland agriculture. The primary goal of this modeling exercise was to investigate whether and to what extent hypothetical changes in factors putatively associated with soil health would affect dryland cotton (Gossypium hirsutum L.) yields. The factors selected were drainage, surface runoff, soil water holding capacity, soil organic carbon (SOC) and albedo. As a first analysis to evaluate these factors, we used the CROPGRO-Cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model. Specifically, we evaluated the effects of reduced surface runoff, increased soil water holding capacity, and SOC, doubling of the soil albedo through stubble mulching, and of soil drainage by enhancing infiltration with no-tillage/cover crops on yield by adjusting related soil properties. In our analysis, we used mean yields simulated with soil properties of a Pullman clay loam soil at Halfway, TX on the THP as baseline, which were compared to values obtained with the adjusted factors using weather data from 2005 to 2019. Simulated mean yield increased by 27% when the soil water holding capacity was increased by 25 mm, 7% when the runoff curve number was decreased from 73 to 60, 16% when soil albedo was increased from 0.2 to 0.4, and by 58% when the soil drainage factor (fraction day−1) was doubled from 0.2. No significant statistical change in simulated mean yield was calculated when SOC was increased by 1%. Further, effects of a 50 mm pre-plant irrigation were also assessed, simulating limited irrigation in the transition to dryland agriculture that resulted in a statistically insignificant 12% increase in seed-cotton yield. Simultaneous implementation of the four statistically significant individual scenarios (increased water holding capacity, infiltration, albedo, and drainage) resulted in the highest increase (93%) in mean seed-cotton yield. An economic and risk analysis of simulated yields under different scenarios indicated that these factors could reduce revenue risk for dryland cotton producers, with most of the effect from soil drainage improvements.
Highlights
The Texas High Plains (THP) is a warm semi-arid region (Köppen-Geiger class BSh) in the southwestern US (Peel et al, 2007)
The single factors that we used in our analysis and that had the largest impact on producer returns is soil drainage rate (S4), TABLE 3 | Simulated mean revenue, certainty equivalent (CE), and probability of large revenue losses for alternative soil health changes for a risk averse Texas dryland cotton producer
The potential dryland cotton yield increases from altering soil drainage, albedo, plant available water, soil organic carbon (SOC), and surface runoff, which are associated with soil health, and pre-plant irrigation were assessed using the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model (CSM) CROPGRO-Cotton model
Summary
The Texas High Plains (THP) is a warm semi-arid region (Köppen-Geiger class BSh) in the southwestern US (Peel et al, 2007). Crop production systems in the THP can be broadly divided into those dependent on irrigation and those typically grown without irrigation. Commercial production of corn, alfalfa, grapes, fruit and nut trees, and even pumpkins is almost exclusively dependent on irrigation. Other crops such as sorghum, sunflower, winter wheat, rapeseed (canola), and sesame are predominately, though not exclusively, grown without irrigation. Cotton (Gossypium hirsutum L.) is a unique crop in the THP and similar regions worldwide in that it has been, and continues to be, grown under a wide range of irrigation regimes ranging from “full” irrigation to exclusively dryland systems. Texas cotton production represents about one third to nearly one half of cotton production in the US, the world’s largest cotton producer (Raper et al, 2020)
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