Scientia Agricultura Sinica ›› 2026, Vol. 59 ›› Issue (16): 3541-3555.doi: 10.3864/j.issn.0578-1752.2026.16.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Identification of Yield-Limiting Factors Associated with Spatiotemporal Differentiation of Soil Nutrients in Long-Term Continuous Sugarcane Cultivation Fields

DENG Jun1,2(), AI Jing2, WANG YuTong2, DAO JingMei2, ZHAO Yong2, YE Song2, DENG Yan3(), ZHANG FuSuo1()   

  1. 1 College of Resources and Environment Sciences, China Agricultural University/National Key Laboratory of Efficient Utilization of Nutrient Resources, Beijing 100094
    2 Sugarcane Research Institute, Yunnan Academy of Agricultural Sciences, Kaiyuan 661699, Yunnan
    3 College of Resources and Environment, Southwest University, Chongqing 400715
  • Received:2026-01-15 Accepted:2026-05-14 Online:2026-08-16 Published:2026-08-17
  • Contact: DENG Yan, ZHANG FuSuo

Abstract:

【Objective】 This study aimed to reveal the spatiotemporal variation in characteristics of soil chemical properties and their relationship with sugarcane yield under long-term continuous cropping, identify the key soil factors limiting sugarcane productivity, and provide a scientific basis for sustainable soil management in sugarcane fields.【Method】 A space-for-time substitution approach was adopted with five continuous cropping duration treatments: CK (0 a), T1 (1 a), T2 (10-19 a), T3 (20-29 a), and T4 (>35 a). Sugarcane yield and soil chemical properties within the 0-60 cm profile (stratified into 0-20, 20-40, and 40-60 cm layers) were systematically analyzed, including pH, soil organic matter (OM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), and available micronutrients contents. The integrated fertility index (IFI) was calculated using factor analysis combined with correlation coefficient analysis and the Nemerow composite index method. Two machine learning algorithms-Extreme Gradient Boosting (XGBoost) and Random Forest (RF)-were employed to quantify the relative contribution of individual soil chemical indicators to sugarcane yield.【Result】 (1) Long-term continuous cropping significantly reduced sugarcane yield (P<0.05), with a 13.68% decline observed in the T4 treatment(>35 years)compared with the CK, and induced deterioration in soil chemical properties. (2) Soil acidification was intensified under continuous cropping, exhibiting significant layer-specific characteristics. Compared with the control (CK), the mean soil pH decreased to 4.60 after more than 20 years of continuous cropping. The subsurface layer (20-40 cm) exhibited the most severe acidification, with pH values significantly lower than the CK in the T3 and T4 treatment groups. (3) Soil nutrients exhibited obvious surface enrichment and accumulation of certain elements. The contents of soil organic matter, total nitrogen, total phosphorus, alkali-hydrolyzable nitrogen, and available phosphorus in the surface layer (0-20 cm) were significantly higher than in deeper layers. Prolonged continuous cropping led to significant accumulation of available potassium and available zinc, increasing by 320.47% and 164.13%, respectively, in the surface layer. (4) The soil integrated fertility index (IFI) decreased significantly with soil depth. Although long-term continuous cropping improved average fertility in the surface layer, it exacerbated the variability and spatial heterogeneity of fertility in deeper soil layers. (5) Both XGBoost and RF analyses consistently identified soil pH in the 20-40 cm layer as the primary limiting factor determining sugarcane yield, with a relative importance contribution substantially greater than that of surface available nutrients and other soil indicators.【Conclusion】 Under long-term continuous sugarcane cultivation, soils exhibited a spatiotemporal soil pattern characterized by surface nutrient enrichment and severe subsurface acidification. Acidification in the 20-40 cm soil layer constitutes the core obstacle restricting sugarcane productivity. In practical production, management strategies should shift from surface-oriented nutrient application to deep amelioration targeting subsurface acidification.

Key words: sugarcane, nutrient balance, soil acidification, subsurface acidification, yield driving factor, machine learning

Fig. 1

Distribution of sampling sites"

Table 1

Historical fertilization practices for sugarcane in Gengma Town"

年份
Year
肥料类型
Fertilizer type
常规施肥次数及施肥量
Fertilization schedule and application rate
施肥总量
Total fertilizer applied (kg·hm-2)
氮磷钾养分总投入量
Total NPK input
(kg·hm-2)
2016—2022 复合肥(总养分44%,N-P2O5-K2O=26-12-6)
Compound fertilizer (total nutrients 44%, N- P2O5-K2O = 26-12-6)
一次性施肥:肥料全部用于基肥施用
Single application: all applied as basal fertilizer
1 200 528
2004—2015 复合肥(总养分25%,N-P2O5-K2O=10-
10-5)+尿素(含N 46%)
Compound fertilizer (total nutrients 25%, N-P2O5-K2O = 10-10-5) + urea (46% N)
二次施肥:基肥施1 200 kg·hm-2复合肥,追肥施600 kg·hm-2尿素
Two applications: basal application of 1 200 kg·hm-2 compound fertilizer; topdressing with 600 kg·hm-2urea
1 800 576
1980—2003 尿素(含N 46%)+过磷酸钙或钙镁磷肥(含P2O5 12%)+氯化钾(含K2O 60%)
Urea (46% N) + single superphosphate or calcium magnesium phosphate (12% P2O5) + potassium chloride (60% K2O)
三次施肥:基肥施300 kg·hm-2尿素+750 kg·hm-2过磷酸钙或钙镁磷肥+300 kg·hm-2氯化钾,第一次追肥施600 kg·hm-2尿素,第二次追肥施300 kg·hm-2尿素
Three applications: basal application of 300 kg·hm-2 urea +750 kg·hm-2 single superphosphate or calcium magnesium phosphate+300 kg·hm-2 potassium chloride; first topdressing: 600 kg·hm-2 urea; second topdressing: 300 kg·hm-2 urea
2 250 822

Table 2

The grading standard of soil properties"

分级
Grade
有机质含量
Organic matter content (g·kg-1)
全氮含量
Total nitrogen
content
(g·kg-1)
全磷含量
Total
phosphorus
content (g·kg-1)
全钾含量
Total
potassium
content (g·kg-1)
碱解氮含量
Alkali-hydrolyzable
nitrogen content
(mg·kg-1)
速效磷含量
Available
phosphorus
content (mg·kg-1)
速效钾含量
Available
potassium
content (mg·kg-1)
pH
>7 <7
Xa 10 0.75 0.4 5 60 3 40 8.5 4.5
Xc 20 1.50 0.6 20 120 10 100 8.0 5.5
Xp 30 2.00 1.0 25 180 20 150 7.5 6.5

Fig. 2

Sugarcane yield (A), sucrose content (B), soil pH (C) and organic matter content (D) in different soil layers under different continuous cropping years Different lowercase letters indicate significant differences among different continuous cropping years for the same indicator (P<0.05). 0 a represents control soil without sugarcane planting; 1 a, 10—19 a, 20—29 a, and >35 a represent continuous cropping for 1 year, 10—19 years, 20—29 years, and 35 years or more, respectively; A, B, and C represent 0-20 cm, 20—40 cm, and 40—60 cm soil layers, respectively. The same as below"

Fig. 3

Soil macronutrient contents in different soil layers under different continuous cropping years"

Fig. 4

Soil micronutrient contents in different soil layers under different continuous cropping years"

Fig. 5

Soil Fertility as influenced by continuous cropping duration and soil depth (0—20, 20—40, 40—60 cm)"

Fig. 6

Performance comparison of XGBoost and RF models for sugarcane yield prediction and importance of soil driving factors A: Relative importance contribution of different soil layers to yield prediction; B: Relative importance of soil nutrient indicators; OM: Organic matter; TN: Total nitrogen; TP: Total phosphorus; TK: Total potassium; AN: Alkali-hydrolyzable nitrogen; AP: Available phosphorus; AK: Available potassium; Fe: Available iron; Mn: Available manganese; Cu: Available copper; Zn: Available zinc; (C) : Importance ranking of key limiting factors, in which the numeric suffix of each variable name indicates the corresponding soil layer depth (e.g., "AN_20-40" denotes alkali-hydrolyzable nitrogen in the 20-40 cm soil layer); (D) : Validation of model performance by fitting predicted values against measured values, where the dashed line represents the 1﹕1 reference line, the solid line represents the linear regression fit, the shaded area represents the 95% confidence interval, R2 is the coefficient of determination, and RMSE is the root mean square error (t·hm⁻2). Orange and blue represent the XGBoost and RF models, respectively"

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