Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin,north-west China

To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

Uncertain and multi-objective programming models for crop planting structure optimization

Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.

Carbon footprint of crop production in Heilongjiang land reclamation area,China

In the context of global warming,agriculture,as the second-largest source of greenhouse gas emissions after industry,had attracted widespread attention from all walks of life to reduce agricultural emissions.The carbon footprint of the planting production system of the Heilongjiang Land Reclamation Area(HLRA),an important commodity grain base in China,was evaluated and analyzed in this paper.On this basis,this paper sought feasible strategies to reduce carbon emissions from two aspects:agronomic practices and cropping structure adjustment,which were particularly crucial to promote the low-carbon and sustainable development of agriculture in HLRA.Therefore,using the accounting methods in IPCC and Low Carbon Development and Guidelines for the Preparation of Provincial Greenhouse Gas Inventories compiled by the Chinese government,relevant data were collected from 2000 to 2017 in HLRA and accounted for the carbon emissions of the planting production system in four aspects:carbon emissions from agricultural inputs,N_(2)O emissions from managed soils,CH_(4) emissions from rice cultivation and straw burning emissions.Then carbon uptake consisted of seeds and straws.Finally,with farmers'incomes were set as the objective function and carbon emissions per unit of gross production value was set as the constraint,this paper simulated and optimized the cropping structure in HLRA.The results showed that there was a“stable-growing-declining”trend in the total carbon emissions and carbon uptake of the planting production system in HLRA,with total carbon emissions of 2.84×10^(10) kg and total carbon uptake of 7.49×10^(10) kg in 2017.In the past 18 years,carbon emissions per unit area and carbon emissions per unit of gross production had both shown a decreasing trend.To achieve further efficiency gains and emission reductions in the planting production system,it was recommended that the local governments strengthen the comprehensive use of straw resources,optimize irrigation and fertilization techniques,and adjust the cropping structure,i.e.,increase the planting area of maize and soybeans and reduce the planting area of rice,and increase subsidies to protect the economic returns of planters.