文献类型： 外文期刊

作者： Eissa, Mamdouh A. ¹ ; Alotaibi, Modhi O. ³ ; Anazi, Hanan Khalaf ⁵ ; Alghanem, Suliman Mohammed Suliman ⁶ ; Qenawy, Mohamed ⁷ ; Ding, Zheli ¹ ; Wang, Hailong ⁸ ; El-Sharkawy, Mahmoud ⁹ ;

作者机构： 1.Chinese Acad Trop Agr Sci, Inst Trop Biosci & Biotechnol, Natl Key Lab Trop Crop Breeding, Haikou 571101, Peoples R China

2.Assiut Univ, Fac Agr, Dept Soils & Water, Assiut 71526, Egypt

3.Princess Nourah bint Abdulrahman Univ, Coll Sci, Dept Biol, POB 84428, Riyadh 11671, Saudi Arabia

4.Princess Nourah bint Abdulrahman Univ, Nat & Hlth Sci Res Ctr, Environm & Biomat Unit, Riyadh, Saudi Arabia

5.Univ Tabuk, Dept Biol, Coll Sci, Tabuk, Saudi Arabia

6.Qassim Univ, Coll Sci, Dept Biol, Burydah 52571, Saudi Arabia

7.Aswan Univ, Fac Energy Engn, Mech Engn Dept, Aswan 81528, Egypt

8.Foshan Univ, Sch Environm & Chem Engn, Foshan 528000, Guangdong, Peoples R China

9.Tanta Univ, Fac Agr, Soil & Water Dept, Tanta, Egypt

关键词： Drought stress; Soil quality; Potassium kinetics; Mango productivity; Machine learning

期刊名称：JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION （ 影响因子：3.1； 五年影响因子：3.6 ）

ISSN： 0718-9508

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Sustainable water stress management in arid and semi-arid regions requires precise understanding of soil-plant interactions when implementing biochar-based strategies. This study developed an Artificial Neural Networks (ANN) model to predict mango productivity under drought conditions using potassium-enriched biochar (KEB), addressing a significant knowledge gap in biochar application modeling. KEB was made by pyrolyzing a maize straw-banana peel mix (1:3) at 500 degrees C for 3 h. A two-year field experiment evaluated four potassium sources, i.e., C (control), KS (K2SO4), KEB (potassium-enriched biochar), and KF (potassium feldspar), under two irrigation regimes representing 80% (normal) and 50% (drought) of available soil moisture. Potassium release patterns between KEB and KS (R-2 = 0.83-0.97), both superior to other treatments. Drought stress significantly impaired soil quality, reducing mango fruit yield by 20% and decreasing soil microbial biomass carbon (MBC) and dehydrogenase enzyme activity by 22% and 14%, respectively. However, KEB application enhanced soil quality under water stress by improving MBC, dehydrogenase enzyme activity, and soil organic carbon (SOC), resulting in an 82% yield increase. KEB treatment also elevated chlorophyll content, proline levels, and soluble carbohydrates, enhancing drought tolerance through improved osmotic adjustment in mango leaves. ANN modeling identified optimal conditions for maximizing fruit yield, with 125% KEB application providing the best results. The model established critical threshold values: SOC (3.1 g kg(- 1)), MBC (365.66 mg kg(- 1)), chlorophyll (3.7 mg kg(- 1)), soluble carbohydrates (38.25 mg kg(- 1)), and phosphorus (2.78 mg kg(- 1)). These findings highlight the dual role of KEB as a sustainable soil amendment and the utility of ANN as a decision-support tool for precision agriculture. Integrating KEB with intelligent modeling approaches offers a promising strategy for improving resilience and productivity in mango orchards under water-limited conditions.