An Optimization-Evaluation Agricultural Water Planning Approach Based on Interval Linear Fractional Bi-Level Programming and IAHP-TOPSIS

An improved fuzzy sorting algorithm coupling bi-level programming for synergetic optimization of agricultural water resources: A case study of Fujian Province, China

The uneven allocation of water resources and the shortage of regional water resources pose great challenges to the economic development and regional development balance of the Fujian province. Optimizing the water allocation structure in different regions can effectively alleviate water pressure. In this study, a type-2 fuzzy bi-level programming (T2FBL) method is proposed to plan the agricultural water resource system in the Fujian province. This method uses an improved fuzzy sorting algorithm to deal with uncertain parameters in the system and combines the bi-level programming method to balance the trade-off between two levels of decision-makers, the uncertain information contained in the secondary membership function omitted in the interval type-2 fuzzy theory is considered in the new ranking algorithm. Multiple scenarios related to different food security needs and different risk indices are examined. The major findings are as follows: (i) With an average tolerance of 75%, the average gross agricultural output value under various scenarios increased (0.4% ∼ 7%) (average 3.89%) after optimization. (ii) The regional water allocation scheme under different food demands and different water availability scenarios is calculated, and the results show that prioritizing adjustments to the industrial water distribution structure of Fuzhou and Zhangzhou will greatly relieve the water pressure in the Fujian province. (iii) The relationship between the availability of system water resources and economic benefits is given through the calculation results of the T2FBL model. These findings can provide an in-depth understanding of the interaction between agricultural, industrial and tertiary industry water allocation and provide technical support for agricultural water resource planning issues.

Agricultural water and land resources allocation considering carbon sink/source and water scarcity/degradation footprint

There is an urgent need for scientific management of agricultural water and land resources to cope with global warming and water shortages. Therefore, a stochastic multi-objective non-linear programming model was established under the society-economy-ecology framework in this study, which is capable of (1) considering the carbon sink function of farmland vegetation and the carbon emissions produced from the input of production materials; (2) dealing with the potential impact of agricultural water use on ecology by the indexes of water scarcity and degradation footprint (3) weighing the conflicts and contradictions among different developing targets with economic benefits, and water productivity as well as ecological considerations; (4) obtaining the planting structure, irrigation water allocation schemes and irrigation schedules. In order to verify the applicability and effectiveness of the model, it was applied to Huangyang Irrigation District in Shiyang River Basin, northwest China. After optimization, the net carbon sink increased by 2.55 × 10⁴ t of which fertilizer contributed nearly 50% of carbon emission, while the water footprint reduced by 0.48 × 10⁸ m³. To analyze the impact of the different allocation schemes of water and land resources on the ecological, economic and social subsystems as well as their interaction, coupling coordination degree (CCD) models were introduced to evaluate status quo and the optimization results of different models. The results showed that, compared with the single-objective models and the status quo, the proposed model has improved the value of CCD from 0.428 to 0.674. The proposed model can promote the harmonious and sustainable development of agricultural production and is equally applicable to agricultural management systems in the regions with similar conditions.

Ranking of problems and solutions in the teaching and learning of object-oriented programming

This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked through the multi-criteria decision methods DEMATEL and TOPSIS in order to determine the best solutions to the problems found and to apply these results in the academic context. The main contribution of this study was the categorization of OOP problems and solutions, as well as the proposal of strategies to improve the problem. Among the most relevant problems it was found: 1) difficulty in understanding, teaching and implementing object-orientation, 2) difficulties related to understanding classes and 3) difficulty in understanding object-oriented relationships. After doing the multicriteria analysis, it was found that the most important solutions to face the problems found in the teaching of OOP were: 1) use of active learning techniques and intrinsic rewards and 2) emphasize on basic programming concepts and introduce the object-oriented paradigm at an early point in the curriculum. As a conclusion, it was evidenced that there is coherence between the literary guarantee that gives support to the problems and solutions in the teaching of OOP presented in this study and the approaches that experts in the area of development highlight as relevant when they identify weaknesses in the process.

Integrated Evaluation of the Water Deficit Irrigation Scheme of Indigowoad Root under Mulched Drip Irrigation in Arid Regions of Northwest China Based on the Improved TOPSIS Method

Limited water resources and low water productivity limit the sustainable development of agriculture in northwest China. In this study, drip irrigation under plastic film was used to achieve an optimal water deficit irrigation (WDI) scheme for the cultivation of indigowoad root (Isatis tinctoria L.). Field water control experiments were conducted in 2016 and 2017. Evaluation of WDI schemes was carried out by considering five indices: water consumption, yield, water use efficiency (WUE), indigo, and (R,S)-goitrin. To enhance the reliability of results, the analytic hierarchy process (AHP) and entropy weight method (EWM) were adopted to calculate the combined weight of the evaluation index. Finally, an improved technique for order of preference by similarity to ideal solution (TOPSIS) that integrated AHP–EWM weights was used to construct a unified, comprehensive evaluation model of indigowoad root under mulched drip irrigation that would produce high yield while saving water. The evaluation results indicated that mild WD (specifically, the V1G1 treatment) was continuously exerted during the vegetative and fleshy root growth periods, which enhanced the WUE and improved the quality of indigowoad root to a certain extent without significantly reducing the yield. These results provide a scientific basis for irrigation of indigowoad in northwest China and other areas with a similar environment.

Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data

To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.