基于产量反应和农学效率的棉花智能化推荐施肥方法研究

doi:10.3724/SP.J.1006.2025.54022

作物学报 ›› 2025, Vol. 51 ›› Issue (11): 3052-3064.doi: 10.3724/SP.J.1006.2025.54022

基于产量反应和农学效率的棉花智能化推荐施肥方法研究

哈丽哈什·依巴提¹,张炎^1,*,李青军¹,徐新朋²,何萍²

1 新疆维吾尔自治区农业科学院农业资源与环境研究所 / 农业农村部荒漠绿洲作物生理生态与耕作重点实验室, 新疆乌鲁木齐830091; 2 中国农业科学院农业资源与农业区划研究所, 北京100081

收稿日期:2025-02-13 修回日期:2025-08-13 接受日期:2025-08-13 出版日期:2025-11-12 网络出版日期:2025-08-25
基金资助:
本研究由国家农业科技项目“农田智慧施肥项目” (20221805), 国家重点研发计划项目(2016YFD0200101)和农业农村部荒漠绿洲作物生理生态与耕作重点实验室开放课题项目(25107020-202104)资助。

Study on smart fertilizer recommendation methods based on yield response and agronomic efficiency for cotton

Halihashi Yibati¹,Zhang Yan^1,*,Li Qing-Jun¹,Xu Xin-Peng²,He Ping²

1 Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences / Key Laboratory of Desert Oasis Crop Physiology, Ecology and Cultivation, Ministry of Agriculture and Rural Affairs, Urumqi 830091, Xinjiang, China; 2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received:2025-02-13 Revised:2025-08-13 Accepted:2025-08-13 Published:2025-11-12 Published online:2025-08-25
Supported by:
This study was supported by the National Agricultural Science and Technology Project “Smart Fertilization Project” (20221805), the National Key Research and Development Program (2016YFD0200101) and the Open Project of the Key Laboratory of Crop Physiology, Ecology, and Tillage in Desert Oasis, Ministry of Agriculture and Rural Affairs (25107020-202104).

摘要/Abstract

摘要：

针对新疆棉花生产中缺乏先进高效的推荐施肥方法和不合理施肥带来的肥料利用率低的现状，本研究以1996—2019年新疆主要植棉区21个植棉县的414个棉花田间肥料试验为基础，建立养分管理大数据库。采用QUEFTS模型模拟棉花最佳养分需求量，并分析土壤基础养分供应、肥料的农学效率与产量反应之间的相关关系，在此基础上构建施肥模型，并开发了适用于新疆棉花生产的养分专家系统。为验证该系统的应用效果，于2017—2021年在新疆主要棉花种植区开展田间验证试验。试验共设6个施肥处理，分别为棉花养分专家系统推荐施肥(NE)，基于NE推荐施肥基础上的不施氮肥、不施磷肥和不施钾肥，农民习惯施肥(FP)和当地的优化推荐施肥(ST)，调查了棉花产量、肥料利用效率和经济效益。QUEFTS模型模拟棉花养分吸收结果表明，每生产1 t籽棉地上部所需氮、磷和钾养分分别为27.7、6.2和29.3 kg。施用氮、磷和钾肥的平均产量反应分别为1624、1096和804 kg hm^?2，平均相对产量分别为0.7、0.8和0.8，平均农学效率分别为6.8、8.5和16.7 kg kg^?1。田间验证结果显示，与FP处理相比，NE处理分别减施氮、磷、钾肥40.7%、60.1%和10.7%；与ST处理相比，NE处理分别减施氮、磷肥30.3%和38.0%，增施钾肥10.8%。与FP和ST相比，NE处理的棉花产量分别增加了365和92 kg hm^?2，经济效益分别增加了4302元 hm^?2和1094元 hm^?2，氮、磷和钾肥回收率分别提高了18.8和11.8、14.2和11.5、13.4和6.0个百分点，氮和磷肥农学效率分别增加了3.5 kg kg^?1和2.2 kg kg^?1、7.2 kg kg^?1和4.4 kg kg^?1，钾肥农学效率分别减少了1.6 kg kg^?1和0.6 kg kg^?1。综上所述，基于产量反应和农学效率构建的智能化新疆棉花养分专家系统，能够为每块地提供个性化的施肥方案。连续多点的田间试验结果充分证明，该方法优化了肥料用量与养分配比，提高了棉花产量和肥料利用率，增加了经济效益，是适用于新疆棉花生产的推荐施肥新方法。

关键词: 棉花, QUEFTS模型, 养分专家系统, 产量, 农学效率, 肥料利用率

Abstract:

To address the low fertilizer use efficiency resulting from the lack of advanced fertilization recommendations and the widespread practice of improper fertilization in Xinjiang’s cotton production, this study established a large-scale nutrient management database based on 414 field fertilizer trials conducted from 1996 to 2019 across 21 major cotton-producing counties. The QUEFTS model was employed to simulate optimal nutrient requirements for cotton and to evaluate the relationships among indigenous soil nutrient supply, fertilizer agronomic efficiency, and crop yield response. Based on these analyses, a quantitative fertilization model was developed, and a field-specific Nutrient Expert (NE) decision support system was designed to suit the conditions of cotton production in Xinjiang. To validate the NE system, field experiments were conducted between 2017 and 2021 in major cotton-growing regions. Each experiment included six fertilization treatments: NE-recommended fertilization (NE); nitrogen (N), phosphorus (P), and potassium (K) omission treatments based on NE; farmer's practice (FP); and locally optimized soil test-based fertilization (ST). Data were collected on cotton yield, fertilizer use efficiency, and economic returns. Model simulations indicated that producing 1 ton of seed cotton requires 27.7 kg N, 6.2 kg P, and 29.3 kg K in above-ground biomass. The average yield responses to N, P₂O₅, and K₂O applications were 1624, 1096, and 804 kg hm^?², respectively; corresponding relative yields were 0.7, 0.8, and 0.8; and agronomic efficiencies were 6.8, 8.5, and 16.7 kg kg^?¹, respectively. Field experiment results showed that the NE treatment applied 40.7%, 60.1%, and 10.7% less N, P, and K fertilizer, respectively, compared to FP, and 30.3% less N and 38.0% less P compared to ST. Compared to FP and ST, the NE treatment increased cotton yield by 365 and 92 kg ha^?1, respectively, and improved economic returns by 4302 and 1094 yuan hm^?². The recovery efficiencies of N, P, and K fertilizers under NE also improved by 18.8 and 11.8, 14.2 and 11.5, and 13.4 and 6.0 percentage points, respectively. Furthermore, the agronomic efficiencies of N and P increased by 3.5 and 2.2 kg kg^?¹, and 7.2 and 4.4 kg kg^?¹, respectively. In contrast, the agronomic efficiency of K under NE decreased by 1.6 and 0.6 kg kg^?¹ compared to FP and ST, respectively. In conclusion, the intelligent, field-specific Nutrient Expert system developed based on yield response and agronomic efficiency offers a tailored fertilization strategy for individual plots. Multi-year, multi-location field experiments demonstrated that this approach optimizes nutrient input and balance, enhances cotton yield and fertilizer use efficiency, and improves economic returns. Therefore, the NE system represents an advanced and practical fertilization strategy for sustainable cotton production in Xinjiang.

Key words: cotton, QUEFTS model, Nutrient Expert system, yield, agronomic efficiency, fertilizer use efficiency

哈丽哈什·依巴提, 张炎, 李青军, 徐新朋, 何萍. 基于产量反应和农学效率的棉花智能化推荐施肥方法研究[J]. 作物学报, 2025, 51(11): 3052-3064.

Halihashi Yibati, Zhang Yan, Li Qing-Jun, Xu Xin-Peng, He Ping. Study on smart fertilizer recommendation methods based on yield response and agronomic efficiency for cotton[J]. Acta Agronomica Sinica, 2025, 51(11): 3052-3064.

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基于产量反应和农学效率的棉花智能化推荐施肥方法研究

Study on smart fertilizer recommendation methods based on yield response and agronomic efficiency for cotton

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