Limited role of soil texture in mediating natural vegetation response to rainfall anomalies

Abstract

Evidence suggests that the response of rainfed crops to dry or wet years is modulated by soil texture. This is a central tenet for certain agronomic operations in water-limited regions that rely on spatial distribution of soil texture for guiding precision agriculture. In contrast, natural vegetation in climatic equilibrium evolves to form a dynamic assemblage of traits and species adapted to local climatic conditions, primarily precipitation in water-limited regions. For undisturbed landscapes, we hypothesize that natural vegetation responds to rainfall anomalies irrespectively of local soil texture whereas rainfed crops are expected to respond to texture-mediated plant available water. Earth System Models (ESMs) often quantify vegetation response to drought and water stress based on traditional agronomic concepts despite fundamental differences in composition and traits of natural vegetation and crops. We seek to test the hypothesis above at local and regional scales to differentiate natural vegetation and rainfed crops response to rainfall anomalies across soil types and better link them to water and carbon cycles. We employed field observations and remote sensing data to systematically examine the response of natural and rainfed cropped vegetation across biomes and scales. At local scales (field to ∼0.1 km), we used crop yields from literature data and natural vegetation productivity as Gross Primary Productivity (GPP) from adjacent FLUXNET sites. At regional scales (∼102 km), we rely exclusively on remote-sensing-based GPP. Results confirm a lack of response of natural vegetation productivity to soil texture across biomes and rainfall anomalies at all scales. In contrast, crop yields at field scale exhibit correlation with soil texture in dry years (in agreement with conventional agronomic practices). These results support the hypothesis that natural vegetation is decoupled from soil texture, whereas rainfed crops retain dependency on soil texture in dry years. However, the observed correlation of crops with soil texture becomes obscured at larger scales by spatial variation of topography, rainfall, and uncertainty in soil texture and GPP values. The study provides new insights into what natural vegetation’s climatic equilibrium might mean and reveals the role of scale in expressing such sensitivities in ESMs.

Determinants of Field‐Saturated Soil Hydraulic Conductivity Across Sub‐Saharan Africa: Texture and Beyond

“…Along the same lines, Niemeyer et al (2014) found that the relationship between woody cover and K fs along a vegetation gradient in dry tropical Nicaragua was greater for fine-textured soils than for coarser soils, which they ascribed to the impact of root-macropore formation on K fs in fine-textured soils. Our results, which differ from these observations, may partly be explained by the so-called "inverse texture effect" observed in dryland ecosystems and by which coarser soils appear to support taller and denser perennial vegetation than finer soils (Gupta et al, 2022;Noy-Meir, 1973). Such apparently contradictory observations indicate that the combined relationship of woody vegetation and soil texture on K fs is complex and requires further investigation.…”

Soil structure and water functions in agricultural soils of the temperate-boreal zone in a changing climate

“…Soils are key for maintaining the productivity of cropping systems. Their ability to retain and transport water ensures water supply during times of demand and the removal of excess water from the root zone to prevent hypoxia or anoxia (Fang and Su, 2019;Gupta et al, 2022a;Kukal et al, 2023). The water storage and transport functions of soils are determined by their hydraulic properties.…”

“…These covariates are listed in Table 4. The relative importance of each covariate for explaining the variation in KL divergence was determined from the random forest analysis, and their non-linear/nonmonotonic effects were derived using partial dependencies (e.g., Gupta et al, 2022a;Jorda et al, 2015). The analysis was applied to three separate scenarios including (i) all samples, (ii) only topsoil samples (soil depth 30 cm; n = 174), and (iii) only subsoil samples (soil depth >30 cm; n = 257).…”