Spatial patterns and key driving factors of wheat harvest index under irrigation and rainfed conditions in arid regions

Abstract

Introduction: The harvest index (HI), a crucial agronomic trait that measures the ratio of grain yield to aboveground biomass, serves not only as a vital indicator for assessing wheat yield but also as a core parameter for predicting straw resource. It reflects the "source-sink" relationship and biomass allocation strategies in crops. However, the spatial distribution patterns of wheat HI and their key driving factors in arid regions remain unclear.

Methods: This study was conducted in Xinjiang, a typical arid region of China, during 2022-2023, involving two years of large-scale systematic sampling. By integrating multidimensional factors such as geographical and climatic conditions, agronomic management practices, and soil nutrient status, methods including correlation analysis, random forest models, structural equation modeling, and linear regression analysis were employed to systematically investigate the spatial distribution characteristics and driving mechanisms of wheat HI under different irrigation regimes in arid regions.

Results: The results revealed that: (1) Wheat HI in arid regions exhibited significant spatial heterogeneity (0.43-0.67), with an overall distribution pattern of "central high, peripheral low" and "northern high, southern low." (2) The importance rankings of influencing factors differed between irrigation regimes. For irrigated wheat, the order of importance was: Geographic-climatic factors, soil nutrient factors, agronomic management factors. Comprehensive analysis identified longitude (lon), plant height (H), latitude (lat), and bulk density (BD) as the key drivers of the Harvest Index (HI) in irrigated wheat. In contrast, for rainfed wheat, the order was: soil nutrient factors, Geographic-climatic factors, agronomic management factors, with total nitrogen (TN), available phosphorus(AP), total potassium(TK), and total phosphorus (TP) emerging as critical drivers of HI.

Discussion: Irrigation significantly enhanced wheat HI (p < 0.01), and irrigated wheat demonstrated significantly higher HI, yield, and aboveground biomass (AGB) compared to rainfed wheat (p < 0.01). Optimizing phosphorus management could enhance HI in both systems, while irrigation infrastructure development remains vital for yield stability. This study provides a theoretical basis and practical guidance for the synergistic multi-objective approach of "yield increase-irrigation-sustainability" in arid regions wheat production.

Keywords: arid region wheat; driving factors; harvest index (HI); irrigation; spatial heterogeneity.

Figure 1

Overview map of the study area and wheat sampling points.

Figure 2

Irrigation effects on wheat productivity. (a–c), HI (a), yield (b), and AGB (c) comparisons between irrigated (irr, blue) and rainfed (non, orange) plots (**p<0.01).

Figure 3

Spatial patterns of wheat HI in arid regions.

Figure 4

Correlation heatmap of wheat HI-associated factors in arid regions (**p < 0.01, *p < 0.05).

Figure 5

Correlation heatmap of wheat HI drivers under different water regimes in arid regions (**p < 0.01, *p < 0.05). (a) Irrigated wheat; (b) Rainfed wheat. Color gradient: red = positive, blue = negative correlations.

Figure 6

Importance ranking of influencing factors in random forest models(**p < 0.01, *p < 0.05. (a) Random forest prediction of factors affecting overall wheat HI in arid regions. (b) Random forest prediction for irrigated wheat HI. (c) Random forest prediction for rainfed wheat HI).

Figure 7

Interactions among factor categories in the PLS-PM structural equation model(**p < 0.01, *p < 0.05. Left panels: Effects and intensity of three factor categories on wheat HI for (a) overall arid regions, (b) irrigated, and (c) rainfed systems. Right panels: Bar plots showing (d) overall, (e) irrigated, and (f) rainfed wheat HI factor effects derived from SEM, including direct, indirect, and total effect magnitudes. Numeric labels on arrows indicate standardized path coefficients. Arrow thickness reflects coefficient magnitude. Color coding: red = positive correlation, blue = negative correlation. Factor importance: Leftward position within each factor category indicates stronger HI influence).

Figure 8

Linear regression analysis between influencing factors and wheat harvest index HI in arid regions. (Subfigure panels: (a) yield(kg/m²) vs. HI. (b) Aboveground biomass (AGB, kg/m²) vs. HI. (c) Plant height (H, m) vs. HI. (d) longitude(°) vs. HI. (e) latitude(°) vs. HI. (f) altitude vs. HI. (g) Soil accumulated temperature (SAT, °C) vs. HI. (h) Total nitrogen (TN, g/kg) vs. HI. (i) Total phosphorus (TP, g/kg) vs. HI. (j) Available phosphorus(AP, mg/kg) vs. HI. (k) Soil water content (SWC, g/cm³) vs. HI. (l) Soil pH vs. HI).