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Soil & Water Management & Conservation

Relationships between Soil–Landscape and Dryland Cotton Lint Yield

^a Dep. of Agricultural and Biological Engineering, Box 9632, Mississippi State, MS 39762
^b USDA-ARS, Crop Science Research Lab., P.O. Box 5367, Mississippi State, MS 39762

^* Corresponding author (ji1{at}ra.msstate.edu)

Topographical land features shape the spatial variability of soils and crop yields, especially in dryland cotton (Gossypium hirsutum L.). The objectives of this study were to (i) quantify the relationships between cotton lint yields vs. derived topographical attributes in combination with measured soil physical properties, and (ii) quantify the relationships between measured soil physical properties and derived topographical attributes. The dominant soil of the study area was classified as Vaiden soil series (very-fine, smectitic, thermic Aquic Dystruderts). More than 4500 elevation point data were recorded in a 42-ha field using a real-time kinematic-global positioning system (RTK-GPS) used in a geographic information system (GIS) to derive topographic (slope, curvature and aspect) and hydrologic attributes (wetness index, flow direction, flow length, flow accumulation, and sediment transport index). Surface (0–17 cm) sand, clay, saturated hydraulic conductivity (K_s), bulk density (_b), water content at seven equilibrium pressure levels ranging from –0.01 to –1.5 MPa, and 2-yr cotton lint yield data were measured from sites selected based on classified normalized difference vegetation index (NDVI). Stepwise linear regression indicated that cotton lint yield variability was explained by soil properties (65% in 2001 and 58% in 2002), and topographic and hydrologic attributes (40 and 21%), as well as their combined effects (82 and 72%). Elevation, flow direction, sediment transport index, percentage sand content, and volumetric water content (_v) at –0.001 MPa pressure explained most of the lint yield variation. Overall, statistical analysis indicated that higher elevation areas generally yielded lower (r = –0.50, P < 0.01) and may experience water stress earlier in the season, as compared with lower elevation areas. We expect that once these features are derived and interpreted, they will have a long-lasting impact on cotton management under dryland conditions.

Abbreviations: _b, bulk density • DEM, digital elevation model • GIS, geographic information system • GPS, global positioning system • K_s, saturated hydraulic conductivity • NDVI, normalized difference vegetation index • OM, organic matter • PAWC, plant available water content • RTK, real-time kinematic