JWSS - Journal of Water and Soil Science

World climate change is an accepted important subject but its negative effects are severe in arid and semi-arid areas of Iran. So, in the present study, two climate scenarios including RCP 8.5 (critical scenario) and RCP 4.5 (moderate scenario) during 2020, 2030, and 2040 decades and their effects on temperature changes in the wheat growth period in five cities of Isfahan province including Isfahan, Najaf Abad, Chadegan, Burkhar, and Meimeh have been investigated. The survey of temperature changes during wheat growth in the next decades showed that Burkhar, Isfahan, Najaf Abad, Chadegan, and Meimeh, respectively will experience more days with a temperature higher than 30°C in 2020, 2030, and 2040 decades than the mean of two recent years (2017-2018). Furthermore, in comparison with present conditions, the most changes in the number of days with a temperature higher than 30°C in next decades climates (2020, 2030, and 2040 decades) will be in Burkhar, Meimeh, Chadegan, Najaf Abad, and Isfahan, respectively. The range of changes percent in the number of days higher than 30°C in next climate conditions rather than present condition will be varied between 5 percent (Isfahan) till 97 percent (Burkhar). The changes percent in all studied cities were more in RCP 8.5 than RCP 4.5. During wheat growth, the number of days less than zero°C will be less in Isfahan, Burkhar, and Meimeh while will be more in Najaf Abad and Chadegan. The evaporation- transpiration will be increased in the next decades during wheat growth. As a result, planning and using compatibility strategies for each city is important to guarantee wheat production.

The effective use of rainwater is a key issue in agricultural development in arid and semi-arid regions. The tillage system as an important soil management measure can affect the rainwater retention, soil moisture content, and in consequence crop yield in rainfed lands. This study was conducted to evaluate the effects of slope gradient and tillage direction on rainwater use efficiency (RWUE) in rainfed lands in Zanjan Province. The field experiment was performed in five slope gradients (12.6, 15.3, 17, 19.4, and 22%) and two tillage directions (along slope and on contour tillage) at two replications. Mass soil water content was determined at 5-day intervals and runoff was measured after rainfalls. Wheat grain yield was determined for each plot and RWUE was computed using the proportion of wheat grain yield and precipitation. Base on the results, runoff, soil moisture, wheat grain yield, and RWUE were affected by tillage directions, so that runoff in contour line tillage decreased about 6.4 times compared to along slope tillage and in consequence increased soil moisture, wheat grain yield, and RWUE about 8.7, 24.8, and 24.8%, respectively. Increasing runoff production in contour line tillage at steeper slopes was associated with a lower capacity of cultivated furrows that strongly declined soil water retention and negatively affected wheat grain yield and RWUE in the lands. This study revealed that the efficiency of the contour tillage in water retention and RWUE decreases in steeper slopes in rainfed lands.

change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.