scholarly journals Multi-Objective Optimization of Smallholder Apple Production: Lessons from the Bohai Bay Region

2020 ◽  
Vol 12 (16) ◽  
pp. 6496
Author(s):  
Shan Jiang ◽  
Hongyan Zhang ◽  
Wenfeng Cong ◽  
Zhengyuan Liang ◽  
Qiran Ren ◽  
...  
Keyword(s):  
Economic Benefit ◽  
Water Footprint ◽  
Bohai Bay ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
P Use Efficiency ◽  
Gray Water ◽  
Use Efficiency ◽  
Apple Production ◽  
Objective Improvement

Transforming apple production to one with high yield and economic benefit but low environmental impact by improving P-use efficiency is an essential objective in China. However, the potential for multi-objective improvement for smallholders and the corresponding implications for horticultural practices are not fully appreciated. Survey data collected from 99 apple producers in Quzhou County of Bohai Bay Region were analyzed by the Pareto-based multi-objective optimization method to determine the potential of multi-objective improvement in apple production. With current practices, apple yield was 45 t ha−1, and the economic benefit was nearly 83,000 CNY ha−1 but with as much as 344 kg P ha−1 input mainly from chemical fertilizer and manure. P gray water footprint was up to 27,200 m3 ha−1 due to low P-use efficiency. However, Pareto-optimized production, yield, and economic benefit could be improved by 38% and 111%, respectively. With a concurrent improvement in P-use efficiency, P gray water footprint was reduced by 29%. Multi-objective optimization was achieved with integrated horticultural practices. The study indicated that multi-objective optimization could be achieved at a smallholder scale with realistic changes in integrated horticultural practices. These findings serve to improve the understanding of multi-objective optimization for smallholders, identify possible constraints, and contribute to the development of strategies for sustainable apple production.

scholarly journals Water–Nitrogen Coupling and Multi-Objective Optimization of Cotton under Mulched Drip Irrigation in Arid Northwest China

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 894
Author(s):  
Xinxin Li ◽  
Hongguang Liu ◽  
Xinlin He ◽  
Ping Gong ◽  
En Lin
Keyword(s):  
Multiple Regression ◽  
Drip Irrigation ◽  
Economic Benefit ◽  
Fiber Quality ◽  
Economic Benefits ◽  
Nitrogen Management ◽  
Multi Objective Optimization ◽  
Nitrogen Application ◽  
Use Efficiency ◽  
Nitrogen Coupling

Cotton is the most important cash crop in Xinjiang but low utilization rate of water and fertilizer is restricting healthy development of this industry. At present, there is a lack of water and nitrogen management optimization methods based on multi-objectives of cotton water use efficiency (WUE), nitrogen use efficiency (NUE), yield, and income. A continuous field experiment was conducted during 2017–2018 to study the effects of water–nitrogen coupling on cotton growth, WUE, NUE, nitrogen partial factor productivity, yield, quality, and economic benefits under drip irrigation in northern Xinjiang. Using multiple regression and spatial analyses, the water and nitrogen management strategy for multi-objective optimization was determined. Three irrigation levels were used—low (I1), medium (I2), and full (I3)—Representing 75%, 87.5%, and 100% of cotton water demand, respectively. The three nitrogen application levels were low (N1, 210 kg/ha), medium (N2, 280 kg/ha), and high (N3, 350 kg/ha), representing 75%, 100%, and 125% of the local nitrogen application, respectively. Among all treatments, the leaf area index, boll weight, dry matter quantity and yield reached respective maxima of 4.43 m2/m2, 4.73 g, 16,623 kg/ha, and 6333 kg/ha for the I3N2 treatment. Cotton fiber quality was the best for I3 irrigation, but too little or too much nitrogen reduced fiber quality. The economic benefit under I3 irrigation was 1.93–4.81 times that for I1. For a single optimization objective, WUE reached a maximum of 1.78 kg/ha·mm for irrigation of 415.80 mm and nitrogen application of 295.71 kg/ha; corresponding single maxima follow: NUE of 37.65% for 418.27 mm and 278.57 kg/ha; yield of 6416.42 kg/ha for 470.12 mm and 304.29 kg/ha; and economic benefit of 15,338.55 RMB/ha for 470.12 mm and 307.14 kg/ha. Multiple regression and spatial analysis showed that for irrigation of 430.71–440.12 mm and nitrogen application of 270.95–318.45 kg/ha, the WUE, NUE, yield, and economic benefits of cotton simultaneously exceeded 90% of their maxima, which was an efficient and reasonable water and nitrogen management mode in this location. The results provide a scientific basis for effective integrated management of water and fertilizer in drip irrigation cotton fields in northern Xinjiang.

scholarly journals Multi-Objective Optimization for Metal Mine Production Technical Indicators with NSGA-II and ANN Algorithms

2018 ◽  
Author(s):  
Xunhong Wang ◽  
Xiaowei Gu ◽  
Qing Wang ◽  
Xiaochuan Xu ◽  
Minggui Zheng
Keyword(s):  
Economic Benefit ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Technical Indicators ◽  
Multi Objective ◽  
Metal Mine ◽  
Mine Production ◽  
Metal Volume

The selection of the best mine production technical indicators is crucial to increasing a mine’s economic benefit and saving resources for sustainability. Therefore, this research proposes a ‘multi-objective optimization model’ based on a ‘fast and elitist Non-dominated Sorting Genetic Algorithm’ (NSGA-II) and ‘Artificial Neural Networks’ (ANN) for the optimization of production technical indicators in the entire geology, mining and beneficiation metal mine production processes. The multi-objective optimization model has decision variables including ‘cut-off grade,’ ‘industrial grade’ and ‘loss rate,’ with objectives being ‘economic benefit (profit)’ and ‘resource benefit (metal volume).’ First, the relationship between the technical indicators of mine production is studied. The REG model, MATLAB’s own ksdensity function and the BP neural network are used to calculate the ore weight, the probability density of grade distribution, the dilution rate, the concentration ratio and the concentrate grade, and to further calculate geological reserves, profit and metal volume. Then, the NSGA-II is applied to maximize profit and metal volume simultaneously. Finally, the model is applied to the Huogeqi copper mine. The optimization result is a set of multiple optimal solutions called Pareto optimal solutions. Compared with the plan data, the profit and metal volume of partial optimization results increased by 2.89% and 2.64% simultaneously. These Pareto optimal solutions can help decision makers in bettering the actual process of metal mine production.

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