Multi-criteria decision analysis integrated with GIS to determine land suitability for cultivation and best harvest time of vine.

BACKGROUND: In this study, the quality of land suitability for vine cultivation in south-western Iran was evaluated and a land suitability map for vine cultivation was developed using the fuzzy-analytic hierarchy process (AHP). The best harvest time of vines was determined based on the relationship between total soluble solids (TSS) and titratable acidity (TA) with fruit colour (red, green, and blue or RGB) in ten points and also the growing degree-days (GDD) maps from April to December. The relationship between GDD and effective parameters in vine cultivation was determined using principal component analysis (PCA) and Pearson correlation methods. RESULTS: The results illustrated that the maximum temperature and relative humidity (RH) have the greatest effect on vine cultivation and its yield (weight 0.24). The results of the land suitability map showed that central regions have better conditions for growing vines (32%). The measurements of TA and TSS depicted that vines of the northern parts have higher TA and lower TSS and there is a significant relationship between them and fruit colour. The results of GDD maps showed that the harvest time of ruby vine and Gezel-azm is July and August, respectively. Also, the temperature (Tmean , Tmax and Tmin ), wind speed, and GDD were the most important parameters to determine the best location for vine cultivation. CONCLUSION: Determining land suitability for cultivation, the best harvest time, the time of grape ripening, and following the exact time of export and import of vine has a vital role to increase its productivity and services. © 2022 Society of Chemical Industry.

Temporal and spatial assessment of groundwater contamination with nitrate by nitrate pollution index (NPI) and GIS (case study: Fasarud Plain, southern Iran).

Groundwater resources in arid and semi-arid regions are the most and sometimes the only water resource used for agricultural, industrial, and urban water supply. Irregular and immense application of nitrogen fertilizers in the lands under cultivation and nitrate leakage from livestock farming have affected the groundwater quality. In such areas, nitrate is one of the main pollutants in the groundwater. In this study, the temporal and spatial trend of nitrate contamination in 31 wells in Fasarud Plain, southern Iran, from April 2017 to March 2018 were assessed. To survey the geochemical quality of the plain, a geographic information system to expand geographic location maps and spatial distribution maps of nitrate concentration and nitrate pollution index (NPI) was applied. Nitrate concentrations ranged between 2.43 and 96 mg L-1. Results indicated that nitrate temporal trend was increased significantly in most of the wells, and the spatial trend of area percentage of nitrate class 3 (not permissible limit of more than 50 mg L-1) was positive. The greatest quantities of this variable in groundwater samples detected in northern, western, and eastern areas of the plain have a direct relation with the fertilization of agricultural lands. Generally, by ending the irrigation season, nitrate concentration and NPI reduced temporally in the samples and the percentage area of nitrate class 3 decreased gradually, again beginning the agricultural season, the NPI, nitrate concentration, and percentage area of nitrate class 3 began to increase. Overall, the change of nitrate concentration and distribution of agricultural regions have illustrated that nitrate originated from nitrogenous inorganic fertilizers applied within irrigation periods.

Modified version for SPEI to evaluate and modeling the agricultural drought severity.

Drought is a climatic phenomenon that can occur in various regions with different climate conditions. Generally, drought has negative impacts on different fields such as environment, rangelands, and water resources. The agricultural section (especially rain-fed agriculture) is one of the parts that is directly affected by different types of drought especially meteorological and agricultural droughts. The standardized precipitation evapotranspiration index (SPEI) is one of the newest and most applied indices to assess drought characteristics. In this paper, a modification is suggested for SPEI with the substitution of observed precipitation (OP) with effective precipitation (EP) to evaluate drought, with an emphasis on consideration of drought effects on agricultural section. To calculate EP, Food and Agriculture Organization of the united nation method (FAO), US Bureau of Reclamation (USBR), the Simplified version of Soil Conservation Service of the US Department of Agriculture method (USDA-SCS simplified), and the CROPWAT version of USDA-SCS method (USDA-SCS CROPWAT) were used. To compare the calculated SPEI based on OP (SPEIOP) and EP (SPEIEP) (based on different EP calculation methods), the correlation coefficients (CC) between SPEIOP and SPEIEP in four synoptic stations with at least 30 years of climatic data and annual yield loss (%) in winter wheat (Triticum sativum) (simulated using AquaCrop model) in the suitable reference periods for agricultural drought were used. Results showed, in Fasa, Drodzan, and Zarghan stations, the CC between SPEI based on EP using the USBR method (SPEIUSBR) and annual YL% had the highest values (in 42.11%, 68.42%, and 36.84% of Triticum sativum all reference periods, respectively). In Shiraz station, the CC between SPEI based on EP using the FAO method (SPEIFAO) and annual YL% had the highest values (in 47.37% of all reference periods). In all stations, the SPEIUSBR had the most reference periods with significant CC at 0.05 or 0.01 levels.

Fuzzy AHP-based spatial distribution of fig tree cultivation in Zaprionus indianus infection risk for sustainable agriculture development.

The spatial distribution of fig trees infected by Zaprionus indianus (ZI) disease, an invasive pest, was analyzed as a control solution to determine the prone area of their growth and cultivation prevention in Southwest Iran. With this aim, the study presented the use of 9 suitability variables for fig tree cultivation mapping in 3 main steps: (i) pre-processing data of each input variable with fuzzy membership function, (ii) land suitability mapping (LSM) by using the pair-wise comparison matrix of analytical hierarchy process (AHP) method and Geographical Information System (GIS) technique, (iii) exclusion layers of Zaprionus indianus from the temperature data and growing degree days (GDD) (from April to October) with the support of inverse distance weighting (IDW) method. The results show that the central regions and parts of the east and northwest of the region (16%) are more suitable for fig cultivation. Compared to 7 growth periods, the insect is more active in the southern parts of the region than in the northern parts. Therefore, it is possible to cultivate figs with high yield in parts of the region where the land is suitable for growing this crop with the lowest activity of ZI. The overlay results show that the suitability distribution of fig cultivation in high and very high levels is mainly in the central regions (13,300 km2, 10%), parts of the east (5320 km2, 4%), and northwest (2660 km2, 2%) of the region. The proposed approach can be useful for management, planners, and local people in the development of agricultural production areas.

Assessment of risk of non-cancer disease in contaminated plant (Ocimum basilicum L.) and soil.

This study tried to conduct an investigation into the rate of contamination by heavy metals (HMs) in both the soil used in the plantation of the basil (Ocimum basilicum L.) as well as the plant itself. The proposed methodology works by assessing the concentrations of 4 heavy metals, inclusive of Pb, Zn, Ni, and Cd. The target hazard quotient (THQ) and the bioconcentration factor (BCF) were deployed for assessing the rate of contamination by HMs within the plant. The plant samples were also analyzed at different stages of growth (DSG) through inspection of their reaction to electromagnetic waves (EW). The results indicated that the THQ was substantially high for Pb and Zn, indicative of the high contamination of the study samples by the metals thereof. The hazard index (HI) for non-carcinogenic hazards was also measured for the entire HMs at 46.64, denoting a high level of contamination in the basil. BCF results also indicated Cd as the most absorbed contaminant (BCF = 1.88) by the target plant. The optimal vegetation index for assessment of HM contamination in the target plant, on the report of the findings, was identified as PD312. Therefore, utilizing EW, the reaction of contaminated plants in DSG is forecastable.

Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model.

Climate change is one of the most important meteorological phenomena that has had a lot of impacts on different sections with different spatial scales. In recent decades, climate changes affected by various factors especially human activities have had various impacts in different sections such as melting glaciers, various flood occurrence, occurrence of different droughts and etc. UNEP aridity Index (UNEP) and Modified De-Martonne index (MDM) are two more used indices to evaluate climate conditions in various regions of the world. In this paper; 1) the temporal trend of changes in climate conditions based on UNEP and MDM indices using climatological data (from 1967 to 2017) of 16 meteorological stations using parametric and non-parametric statistical tests were evaluated 2) the accuracy of UNEP and MDM indices were compared to assess climate conditions, based on the correlation between mentioned indices and percent of annual yield loss (AYL) in rain-fed winter wheat using simple and multiple Generalizes Estimation Equation (GEE) methods (for help managers to select more suitable and more accurate index to assess climate condition). Results showed, based on UNEP and MDM indices, climate indices in 93.75% and 87.5% of stations had a decreasing trend, but decreasing trend only in 56.25% and 50% of stations were significant at 5% level (respectively). The evaluation of the accuracy of UNEP and MDM indices showed that, in all stations, |B| coefficients between calculated AYL and UNEP and MDM indices and R2 coefficients between simulated AYL using AquaCrop model and predicted AYL using simple and multiple GEE methods in UNEP aridity index were more than MDM index. So, it is recommended to use UNEP aridity index to assess the climate conditions in different regions.