A novel mechanism to simulate intercropping and relay cropping using the DayCent model

Estimation of greenhouse gas emission flux from agricultural lands of Khuzestan province in Iran

Greenhouse gas emissions and their effects on global warming are one of the serious challenges of developed and developing countries. Investigating the amount of greenhouse gas emissions of different countries makes it possible to determine the share of countries in the production of greenhouse gases. The purpose of this study is to use DAYCENT and DNDC models to estimate the emission rate of methane, nitrous oxide, and carbon dioxide greenhouse gases as well as to estimate the global warming potential in Khuzestan agricultural lands in Iran. For this purpose, the gas sampling was done in rice, wheat, and sugarcane fields using a static chamber, and then the concentration of methane, nitrous oxide, and carbon dioxide was determined by using gas chromatography. In the following, DAYCENT and DNDC models were used to estimate gas emissions and the global warming potential of these gases was estimated. Finally, TOPSIS method was used to prioritize gas emissions. In order to evaluate the modeling accuracy, the statistical indicators of maximum error, root mean square error, determination coefficient, model efficiency, and residual mass coefficient were used. According to the results, the highest measured gas flux was obtained for rice fields at Baghmalek and the lowest for sugarcane in Abadan. The results of DAYCENT model estimation showed that the highest emissions were obtained for methane gas and rice cultivation, and lowest gas emissions were obtained for sugarcane cultivation. The results of DNDC model estimation also showed that the highest flux was determined for nitrous oxide gas in rice cultivation. The results of the estimation of global warming potential also showed that it was the highest in sugarcane cultivation (Shushtar station) and the DAYCENT model, and the lowest was also in wheat cultivation and the DNDC model. The statistical results of the estimation of DAYCENT and DNDC models showed that the DAYCENT model in sugarcane cultivation (Shushtar station) was the most accurate in estimating carbon dioxide gas, and the lowest accuracy was related to the DNDC model and sugarcane cultivation (Shushtar station) in estimating nitrous oxide gas. According to the results of agricultural activities in Khuzestan province, they have made a major contribution to the production of greenhouse gases, which, or the lack of attention to this issue, will have an effect on the future climate of this region.