Search

The Sistan region in Southeastern Iran is one of the world's most sensitive areas when it comes to sandstorms and wind erosion. One of the most influential factors in interpreting sandstorms is sand drift potential (DP), which is directly related to wind speed. Accurately, monitoring this phenomenon is still being determined, considering various temporal scales. Therefore, the main aim of this research is to analyze the trend of DP on monthly and annual scales. Our results showed that monthly variations of DP reached the highest and lowest values in July (609 VU) and January (47 VU), respectively. Blowing sand predominantly moved southeast, and the directional index fluctuated from 0.88 to 0.94. The annual DP was measured equal to 2700 VU, signifying a relatively high value when compared to other arid regions worldwide. The trend analysis results obtained from the Mann–Kendall test revealed both positive trends during the period 1987–2001 and negative ones from 2002 to 2016). However, the positive trend was found statistically insignificant. Furthermore, Sen's slope test results demonstrated that a negative trend could be observed with a steeper slope during July, September, and August, while a positive trend could be observed with a steeper pitch during November, December, and June. We recommend that land managers and stakeholders involved in controlling blowing sand using biological and physical methods should consider these trends in the Sistan region. Implementing nature-based solutions or control strategies should focus on these temporal sequences. [ABSTRACT FROM AUTHOR]

Agricultural lands are sources and sinks of greenhouse gases (GHGs). The identification of the main drivers affecting GHGs is crucial for planning sustainable agronomic practices and mitigating global warming potential. The main aim of this research was to evaluate the impact of environmental drivers (soil temperature and water-filled pore space, WFPS) and crop residue rates on CO 2 , NO, and NO x fluxes under conventional tillage (CT) and no-tillage (NT) systems. The accuracy of Random Forest Regression (RFR), Multiple Adaptive Regression Splines (MARS), and General Linear Models (GLM) in predicting CO 2 , NO, and NO x fluxes were also assessed. In both CT and NT systems, CO 2 , NO, and NO x fluxes decreased with increasing WFPS. Increasing temperature resulted in higher CO 2 emissions and lower NO and NO x emissions. Higher residue rates resulted in significant increases in CO 2 emission, whereas the NO and NO x emissions increased by decreasing the ratio of residue. For CO 2 prediction, the RFR provided the largest R2 with the observed data. For NO–N and NO x -N prediction, RFR was the most efficient algorithm, but NO–N can be predicted with better accuracy. The output of this research highlights the importance of agronomic practices for climate mitigation, along with the possibility of using RFR to predict GHGs fluxes. • Increasing temperature increased CO 2 emissions and decreased NO and NO x emissions. • The highest NO and NO x emissions were observed in the residue removal treatment. • Increasing WFPS decreased CO 2 , NO, and NO x emissions. • The accuracy of the machine learning algorithm methods (RFR, MARS, and GLM) was evaluated. • RFR (among RFR, MARS, and GLM) had the highest accuracy in predicting emissions. [ABSTRACT FROM AUTHOR]

Appropriate management of crop residue plays a key role in mitigating greenhouse gas emissions. However, it has been inadequately implemented in general agricultural management practices. In a field investigation using static chambers, we evaluated the effects of crop residue at three different rates - 100 % (R 100), 50 % (R 50), and residue removal (R 0) - on carbon dioxide (CO 2) efflux. The field study was conducted in corn-wheat rotation under conventional (CT) and no-tillage (NT) systems in a semi-arid region. The main results showed that CO 2 efflux was positively correlated with higher soil temperature (0.43–0.79) and microbial biomass carbon (0.66–0.89). The crop residue treatments affected these traits. A strong positive relationship between CO 2 efflux and the crop residue (R2 = 0.96, CT and R2 = 0.9 for NT) was observed. In the CT system, significant increases were detected among residue rates on cumulative CO 2 efflux, where R 100 and R 50 resulted in 36 % and 25 % higher cumulative CO 2 efflux, respectively, than R 0. In contrast, there was no significant difference in cumulative CO 2 efflux among the crop residue retention (R 100 and R 50) and removal (R 0) treatments under the NT system. Our study revealed that crop residue retention led to increased CO 2 efflux under the CT system in semi-arid conditions during the first year of application, while under the NT system, CO 2 efflux was not impacted by crop residue. Our results indicate that there is considerable potential for improving soil management practices in the context of soil degradation, climate change, increasing crop productivity, and carbon (C) sequestration. • CO 2 emissions are correlated with soil temperature and microbial biomass carbon as well as with crop residue. The crop residue treatments affect these traits. • Full retention of crop residue mitigates CO 2 emissions under the no-tillage system. • The implementation of the results improves the conservation of soils' productivity, and fights climate change. [ABSTRACT FROM AUTHOR]