Climate variability impacts on rainfed cereal yields in west and northwest Iran

Impact of Different Fertilizer Sources under Supplemental Irrigation and Rainfed Conditions on Eco-Physiological Responses and Yield Characteristics of Dragon's Head (Lallemantia iberica)

The effects of the irrigation regime and different fertilizer sources on the eco-physiological responses and yield characteristics of dragon's head were explored in a factorial experiment based on a randomized complete block design with 12 treatments and 3 replications in the 2019 growing season. The treatments included six different fertilizer sources (animal manure, vermicompost, poultry manure, biofertilizer, chemical fertilizer, and control) and two irrigation regimes (rainfed and supplemental irrigation). The results indicated the positive effects of supplementary irrigation and the application of vermicompost, poultry manure, and animal manure by increasing the absorption of nutrients (phosphorus and potassium) and improving relative water contents, chlorophyll and carotenoid contents, and the fixed oil percentage of dragon's head. The activities of catalase, ascorbate peroxidase, and superoxide dismutase decreased in the rainfed plants, whereas organic fertilizer application increased the antioxidant enzyme activity. The highest grain yield (721 kg ha^-1), biological yield (5858 kg ha^-1), total flavonoids (1.47 mg g^-1 DW), total phenol (27.90 mg g^-1 DW), fixed oil yield (200.17 kg ha^-1), and essential oil yield (1.18 kg ha^-1) were noted in plants that were treated with vermicompost under supplemental irrigation. Therefore, it is recommended that organic fertilizers such as vermicompost and poultry manure be used to substitute chemical fertilizers. These practices can help popularize organic crops using rainfed and supplementary irrigation.

Drought, Climate Change, and Dryland Wheat Yield Response: An Econometric Approach

Agriculture has been identified as one of the most vulnerable sectors affected by climate change. In the present study, we investigate the impact of climatic change on dryland wheat yield in the northwest of Iran for the future time horizon of 2041-2070. The Just and Pope production function is applied to assess the impact of climate change on dryland wheat yield and yield risk for the period of 1991-2016. The Statistical Downscaling Model (SDSM) is used to generate climate parameters from General Circulation Model (GCM) outputs. The results show that minimum temperature is negatively related to average yield in the linear model while the relationship is positive in the non-linear model. An increase in precipitation increases the mean yield in either model. The maximum temperature has a positive effect on the mean yield in the linear model, while this impact is negative in the non-linear model. Drought has an adverse impact on yield levels in both models. The results also indicate that maximum temperature, precipitation, and drought are positively related to yield variability, but minimum temperature is negatively associated with yield variability. The findings also reveal that yield variability is expected to increase in response to future climate scenarios. Given these impacts of temperature on rain-fed wheat crop and its increasing vulnerability to climatic change, policy-makers should support research into and development of wheat varieties that are resistant to temperature variations.

Probability assessment of vegetation vulnerability to drought based on remote sensing data

Drought is one of the important factors causing vegetation degradation. Determination of areas with vegetation more sensitive to drought can be effective in drought risk management. Considering the ability to describe vegetation conditions, vegetation health index (VHI) was used to determine the probability of vegetation vulnerability to drought and to provide the map of Iran showing sensitive areas to drought. This study tries to express the probability of vegetation vulnerability to drought in four main climatic classes including hyper-arid, arid, semi-arid and semi-humid, and humid in Iran. Temperature condition index (TCI) and vegetation condition index (VCI) were calculated using land surface temperature (LST) derived from the MOD11A2 product and normalized different vegetation index (NDVI) obtained from MOD13A2 product, MODIS sensor. Combining these two indices, VHI was calculated for late of March, April, May, and June during 2000-2017. VHI was classified into five classes representing the drought intensity. Then, the probability of occurrence (%) of each class was calculated and multiplied with weight of each class, varying from 0 to 40 based on drought intensity. Finally, probability of vegetation vulnerability index (PVVI) was calculated by summing of the values obtained for each class. The results showed that PVVI was higher in arid and hyper-arid areas than that in other areas in the four studied periods. The highest mean values of PVVI in humid as well as semi-arid and semi-humid classes were found in April as 59.87 and 62.4, respectively, while the highest mean values of PVVI in arid and hyper-arid classes were observed in May as 70.98 and 68.13, respectively. In total, our results showed that PVVI is affected by different climatic and topographic conditions, and it suggested that this index be used to determine the probability of vegetation vulnerability.

Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment

The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000-2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI_max were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r² ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.

Past crops yield dynamics reconstruction from tree-ring chronologies in the forest-steppe zone based on low- and high-frequency components

Interrelations of the yield variability of the main crops (wheat, barley, and oats) with hydrothermal regime and growth of conifer trees (Pinus sylvestris and Larix sibirica) in forest-steppes were investigated in Khakassia, South Siberia. An attempt has been made to understand the role and mechanisms of climatic impact on plants productivity. It was found that amongst variables describing moisture supply, wetness index had maximum impact. Strength of climatic response and correlations with tree growth are different for rain-fed and irrigated crops yield. Separated high-frequency variability components of yield and tree-ring width have more pronounced relationships between each other and with climatic variables than their chronologies per se. Corresponding low-frequency variability components are strongly correlated with maxima observed after 1- to 5-year time shift of tree-ring width. Results of analysis allowed us to develop original approach of crops yield dynamics reconstruction on the base of high-frequency variability component of the growth of pine and low-frequency one of larch.