Intense Pasture Management in Brazil in an Integrated Crop-Livestock System Simulated by the DayCent Model

Simulating soil C dynamics under intensive agricultural systems and climate change scenarios in the Matopiba region, Brazil

The recent agricultural expansion in the Matopiba region, Brazil's new agricultural frontier, has raised questions about the risk of increasing soil organic carbon (SOC) loss as large areas of native vegetation (NV; i.e., Cerrado biome) have been replaced by large-scale mechanized agriculture. Although sustainable managements, such as integrated crop-livestock (ICL) systems, are considered strategic to counterbalance the SOC loss associated with land-use change (LUC) while keeping food production, little is known about their long-term effects on SOC stocks in the Matopiba region. To this end, we used the DayCent model to simulate the effects of converting the management commonly used in this region, i.e., soybean-cotton rotation under no-tillage (NT), into ICL systems with distinct levels of intensification (e.g., crop rotations: soybean-pasture and soybean-pasture-cotton; soil and crop management: grass irrigation, scarification/harrowing, and length of grass cultivation) on long term SOC dynamics. Additionally, data from two projected climate scenarios: SSP2-4.5 [greenhouse gases emissions (GHG) will not change markedly over time and global temperature will increase by 2.0 °C by 2060] and SSP5-8.5 (marked changes in GHG emissions are expected to occur resulting in an increase of 2.4 and 4.4 °C in global temperature in the middle and at the end of the century) were included in our simulations to evaluate climate change effects on SOC dynamics in this region. Based on a 50-yr-time frame simulation, we observed that SOC stocks under ICL systems were, on average, 23% and 47% higher than in the NV (36.9 Mg ha-1) and soybean-cotton rotation under NT (30.9 Mg ha-1), respectively. Growing grasses interlaid with crops was crucial to increase SOC stocks even when disruptive soil practices were followed. Although the irrigation of grass resulted in an early increase of SOC stocks and a higher pasture stoking rate, it did not increase SOC stocks in the long term compared to non-irrigated treatments. The SSP2-4.5 and SSP5-8.5 climate scenarios had little effects on SOC dynamics in the simulated ICL systems. However, additional SOC loss (∼0.065 Mg ha-1 yr-1) is predicted to occur if the current management is not improved. These findings can help guide management decisions for the Matopiba region, Brazil, to alleviate the anthropogenic pressure associated with agriculture development. More broadly, they confirm that crop-livestock integration in croplands is a successful strategy to regenerate SOC.

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.