جستجوی مقالات مرتبط با کلیدواژه « عملکرد محصول » در نشریات گروه « آب و خاک »

The Nirvin variety is one of Iran's most cultivated varieties of bell peppers. Still, no specific dietary and irrigation program has been determined to achieve the maximum yield and water use efficiency. To determine the optimal irrigation for greenhouse cultivation of this variety, an experiment was conducted in a completely randomized design with three replications based on irrigation treatments of 100 (I0), 85 (I1) and 70 (I2)% water requirement in the research greenhouse of the Faculty of Agricultural Sciences, University of Guilan in 2022-2023. The results showed no significant difference in final yield traits and water use efficiency between the 100 and 85% water requirement, with the reduction of irrigation to 70% water requirement, these traits decreased by 21.43, and 8.52%, respectively. The results related to the amount of ionic leakage and free proline content indicated that reducing the irrigation to 85% of the water requirement did not cause water stress in the plant. The reduction of irrigation led to an increase in the length of the fruit stems and a decrease in their thickness. The highest number of fruit lobes was observed at the 85% water requirement which was 13.96 and 34.89% more than 70 and 100% water requirement, respectively. A strong and significant negative correlation was observed between the traits of fruit length, fruit volume, vitamin C, and antioxidant capacity and the set of traits of fruit weight, water use efficiency, relative water content, leaf chlorophyll, and fresh plant weight. Based on the results of this research, changing irrigation from 100 to 85% water requirement not only saves water but also improves the plant's resistance to common problems such as blossom-end rot and increases the shelf life of the fruits. So, an 85% level of irrigation is recommended for this variety of pepper under controlled conditions.

Soil properties and management methods can affect plant growth and crop yield in agricultural areas. Rainfed agriculture is a dominant farming type in the world, which covers about 80% of the world's cultivated land and about 67 % of agricultural lands in Iran. Most of rainfed lands are in slope areas and tilled along slope, an incorrect method which accelerates water erosion. Controlling soil erosion and storing precipitation water in soil is the first step to conserve soil and water resources and increasing crop yield in rainfed lands. The change of tillage direction from along slope to contour line and maintaining crop residues can affect water loss, Precipitation use efficiency (PUE) and crop yield in rainfed areas. This study was carried out to investigate the effect of tillage direction and wheat straw mulch on water loss, crop yield and precipitation used efficiency in a winter wheat rainfed land.

The study was carried out in a rainfed lands with sandy loam soil under a slope of 10% in west of Zanjan, North West of Iran. field experiments were conducted at two tillage direction: along slope and contour line, and five wheat straw mulch application levels (control, 25, 50, 75 and 100%) with three replications. Precipitation data (height, duration and intensity) was taken from the Agricultural Meteorological Station located in the University of Zanjan, about 800 m from the study field. Winter wheat was cultivated at the plots early autumn and harvested early summer. Water loss from the plots was measured for each rainfall event resulting runoff at the plots during nine months growth period. Wheat grain yield was determined for each plot and accordingly precipitation use efficiency (PUE) in kg/ha.mm was computed based on wheat grain yield (kg/ha) per effective precipitation during growth period (mm). Soil properties and winter straw mulch characteristics were determined using the conventional methods in the lab and the variance analysis was used for determination of independent effect of tillage direction and straw mulch level and interactions between the two factors.

Eighty-two precipitation events were occurred in the area during a 9 months winter wheat growth period, which rainfalls included 88% from it. Most of rainfalls occurred in October that resulted most water loss at the plots. Water loss at the plots tilled contour line was 14% lower than the plots tilled along slope. However, water loss between the two tillage directions was no significant, whereas wheat grain yield as well as PUE were significantly affected by tillage direction. Straw mulch considerably affected on water loss, wheat grain yield and PUE. An obvious decrease was found in water loss with increasing in straw mulch level. The lowest water loss among different mulch levels was in 100% mulch level for the two tillage directions (63% for along slope tillage and 64% for contour line tillage). Significant interaction of the two factors was observed just for wheat grain yield. The highest wheat grain yield was in 75% mulch level which was about 27% (2.04 ton/ha) and 34% (2.45 ton/ha) bigger than control treatment, respectively. The highest PUE was also in 75% mulch level both in along slope tillage (7.2 kg/ha.mm) and contour line tillage (8.6 kg/ha.mm), which was 27% and 34% higher than control treatment, respectively.

Results show the precipitation use efficiency (PUE) is an improper index for evaluation of the role of precipitation in crop production in wheat rainfed lands. This index can be affected by both tillage direction and wheat straw mulch application in rainfed lands. These two management methods are independent factors which affects strongly on the wheat grain yield and in consequence on the PUE. The change of tillage direction from along slope to contour line along and application of 75% mulch level (equal to 4.5 ton/ha) are proper strategies for conservation of soil and water and improving the utilization of precipitation in wheat rainfed lands.

Due to population growth and Iran's location in arid and semi-arid regions of the world, the need for water and food has increased and as a result, the pressure on water and soil resources will be more than before. On the other hand, the risk of drying up Lake Urmia, which causes environmental problems in the region, requires macro-water planning for the region and the use of optimal cultivation pattern to deal with water scarcity. Therefore, optimal use of water preserves water resources and increases the quality of products. Today more than ever, increasing the production of strategic crops such as wheat requires the proper use of water resources. The main source of food for the Iranian people is wheat and related products, and any action that increases the yield of wheat due to limited soil resources, especially water resources, is important and necessary at the same time. In recent years, significant advances have been made in modeling product growth and development using mechanical models. Plant growth models are increasingly used in the analysis of agricultural systems and simulate the plant's response to growth factors using mathematical equations. The AquaCrop model is one of the dynamic and user-friendly models developed by the FAO. The AquaCrop model receives information about farm, plant, soil, irrigation and climate, and ultimately predicts important parameters such as crop. Wheat yield simulation allows efficient management and better planning under various environmental inputs such as soil and water. To achieve higher accuracy and less model error, field parameters must be properly calibrated by the model to achieve proper performance. Also, calibration of the model, if not done correctly, causes a high error prediction by the model, which leads to incorrect management, water loss, plant drought and other cases. Therefore, using a model that has accurate and close prediction to the AquaCrop model and requires fewer input parameters is essential, which saves time, reduces costs and eliminates calibration errors. However, this model requires relatively large input parameters and is a time-consuming model in the presence of multiple scenarios.3

In recent years, smart models have been able to show high accuracy and become reliable models. Therefore, in the present study, to solve this problem and develop a model with less input data, using the ANN, SVR and SVR-FFA intelligent models and creating 440 scenarios in 2 farms, the performance of the AquaCrop model was compared.99WestW2 farm is located in Miandoab city and has a yield of 6.588 (ton ha-1) and WestW10 farm is located in Mahabad city and has a yield of 5.05 (ton ha-1).

The results of the model are performed using 5 evaluation criteria of Correlation coefficient, Root mean square error, Nash-Sutcliffe coefficient, Wilmot’s index of agreement and, Mean absolute percentage error. The results of this study showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model could have the lowest error rate, so that for the yield index, the RMSE value for the mentioned farms was 0.033 and 0.069 (ton ha-1), respectively. The use of three models SVR, SVR-FFA, and ANN and their comparison with the AquaCrop model to predict wheat yield has been done for the first time in this study. The SVR model was able to show the highest accuracy after the SVR-FFA model. For 99WestW2 farm, it can reduce the error rate to 0.043 (ton ha-1) and for WestW10 farm to 0.077 (ton ha-1) and show good performance. The ANN model, after the SVR model, was able to show acceptable accuracy. The ANN model for 99WestW2 farm was able to reduce the error rate to 0.123 (ton ha-1) and for WestW10 farm to 0.094 (ton ha-1). Finally, the ANN model had a relatively higher error than the SVR-FFA and SVR models, respectively, and showed a relatively lower performance than the two models.

Finally, the intelligent SVR-FFA, SVR and ANN models, despite having the least number of inputs, were able to predict yield values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. For further studies, it is suggested that the ANN model be combined using the firefly algorithm (MLP-FFA) to increase the accuracy of the ANN model and make more accurate predictions of wheat yield.

Evaporation and Transpiration (including soil water evaporation and transpiration in plants) account for about 60% of the annual atmospheric precipitation on the earth. This process is the backbone of the hydrological cycle and a key element in Water Resources Management and the agricultural sector. The Soil and Water Assessment Tool (SWAT) and the Surface Energy Balance Algorithm for Land (SEBAL) are specialized models that address this problem. A SEBAL model is a tool for estimating the spatial distribution of actual evaporation and transpiration using satellite images such as MODIS, and Landsat 8. Although Landsat 8 images have a higher Spatioal resolution than MODIS images (30 m vs. 1000 m), their Tempoural resolution is lower than that of MODIS images (every 16 days vs. every day). On the other hand, daily MODIS images may not always be usable because of problems such as cloudiness. In addition, the interpretation of numerous satellite images is very time-consuming. In the current study, the evaporation and transpiration values in the catchment area of Karun River were obtained in three dry, normal and wet years (2019, 2020, 2021) using the recalibrated SWAT model based on runoff and crop yield and the SEBAL algorithm. Then the results of the SEBAL algorithm and SWAT model were compared with each other based on the water year conditions. In the second stage of the research, the results of the SEBAL algorithm and the range of changes of the main parameters of this algorithm in the plains of Ahvaz-Mlathani and Al-Baji basins were presented according to the ground data and using the MODIS images from the Terra satellite with a suitable time resolution and OLI sensor from Landsat8 satellite, which has a good spatial resolution.

Water scarcity is the most important factor limiting the production of agricultural products, especially in arid and semi-arid regions. Application of some materials such as superabsorbent polymers in soil increases water retention in soil and thus reduces water consumption and fertilizer leaching. These materials can reduce the effects of dehydration on the plant and lead to increased yield in arid and semi-arid regions. In order to investigate the effect of superabsorbent under water stress conditions on yield, yield components and some physiological characteristics of corn, experimental plots were conducted in the form of split plots in a randomized complete block design in Gotvand region in Khuzestan province. Three treatments of Aquasorb 3005 (600,300,0 kg / ha) A1, A2 and A3 and four irrigation strategies (50,75,100,125) percent of water requirement I1, I2, I3 and I4 in three replications for two growing seasons, respectively Summer 2019 and spring planting 2020) were considered. Irrigation planning, biological yield, harvest index and canopy cover were measured at different stages of growth seasons, based on which a growth simulation model was presented using AquaCrop software. The results of this test showed that the use of Aquasorb 3005 increased product performance by one percent. In addition, the results show the effect of superabsorbent application and irrigation planning on improving water efficiency. The highest grain yield in spring and summer cultivation was related to I2A3 treatment equal to 9055 and 9255 kg / ha, respectively, and the lowest yield in both crops was related to I4A0 treatment equal to 5977 kg / ha in spring cultivation and 6344 kg / ha in summer cultivation.

Rosa Damascene Mill. is cultivated in the most parts of Iran. This flowers are worldwide well known for their fine fragrance and are harvested to produce rose oil used in perfumery or to make rose water. Due to the lack of water resources in the world, especially in arid and semi-arid areas such as Iran, accurate estimates of the crop water requirements are necessary and essential for planning and managing irrigation events in the field. One method among the many available ones for estimating the crop evapotranspiration is the use of the reference evapotranspiration (grass) and crop coefficients. Crop coefficients can be variable due to the differences in plant variety, climatic conditions and the type of water, soil and plant managements. Therefore, for a more accurate estimate of crop evapotranspiration, local crop coefficients should be derived and used. The purpose of this research is to determine the crop coefficients of the Rosa Damascene Mill. under a two-row drip irrigation system using the soil water balance method in the Ghamsar region.

This research was conducted during 2022 in a 53-hectare Rosa Damascene Mill. farm in the Ghamsar city. The soil moisture content prior and after each irrigation event was measured by gravimetric method. The actual evapotranspiration of the Rosa Damascene Mill. was calculated during the growing season as the residual term of the soil water balance equation. The crop coefficient was obtained by dividing the actual evapotranspiration of the Rosa Damascene Mill. by the reference evapotranspiration. ET0 Calculator and Crop Wat software were used to calculate reference evapotranspiration by the FAO- Penman- Monteith equation using the daily meteorological data recorded at the nearest weather station. In order to evaluate the usefulness of the irrigation water consumed, the water productivity indicators were determined. Using 8 volumetric flow meters that were installed at the beginning of 8 irrigation trickle lateral pipes in different parts of the field, the volume of water entering 4 control-rows of Rosa Damascene Mill. during the growing season was measured. Also, harvested flowers of the Rosa Damascene Mill. (YC) in each one of these control-rows were weighed separately and irrigation water productivity and water use productivity were determined for each one of the control-rows.

The Crop Wat and ET0 Calculator software estimated the seasonal reference evapotranspiration during the growing season to be 1147 and 1155 mm, respectively. The depth of water consumed by the Rosa Damascene Mill. in the wetted area by the water coming out of the emitters during the 188-day growing season of Rosa Damascene Mill. was 997 mm. The cumulative depth of water infiltrated into the wetted area was equal to 1017 mm. It should be noted that the depth of water consumption in the wetted area is not equal to the actual evapotranspiration of the Rosa Damascene Mill., because the actual evapotranspiration of the crop was calculated per unit of field area (sum of wetted and non- wetted areas), while the depth of water consumption in the wetted area was calculated. The minimum and maximum crop coefficients during the growing season were 0.6 and 1.2, respectively. The weight of the wet Rosa Damascene Mill. flower harvested from the entire farm, which is about 40 hectares cultivated out of the 53 hectares total, was equal to 130 tons. Therefore, the average yield of wet Rosa Damascene Mill. flower in the 40 hectares cultivated area was 3.25 tons per hectare. The highest amount of irrigation water productivity obtained from a control-row was 0.81 kg/m3. The average yield of Rosa Damascene Mill. flower in the evaluated control-rows was 48.25 kg per row, where the average irrigation water productivity was 0.78 kg/m3.

The reported crop coefficient values in the Karaj region are higher than the crop coefficient values obtained in the Ghamsar region during most of the growing season. This difference can be attributed to the differences in the climate and the type of irrigation system used, the type of crop variety, and the way the crop was managed. With the increase of irrigation volume in the studied rows, it was observed that the yield and irrigation water productivity increased. By generalizing the conditions of the control-row A to the whole farm, it was determined that if the whole farm was irrigated with a volume of 156,000 m3 during the growing season, the yield of the whole farm would had reached about 130 tons. By generalizing the conditions of control-row D to the whole farm, it was determined that if the whole farm was irrigated during the growing season with a volume of 148,725 m3, the yield of the whole farm would had been about 110 tons. Therefore, by saving 7275 m3 of irrigation water, the yield of the entire farm would decrease by about 20 tons.

Onion, similar to most vegetables, is sensitive to inadequate irrigation. Onion has a shallow root and should be irrigated frequently with a small amount. Uniform and constant soil moisture during the growth period is very useful for onion growth. Excessive irrigation, in addition to reducing crop growth, increases costs, wastes water, reduces water productivity, and reduces product storability. Onion is one of the important agricultural products in Fars province. So far, little information has been published on the irrigation status and water productivity of onion fields. Knowing the amount of water required for irrigation and the water productivity of onion farms can help national and provincial planners and managers to increase production and save water more effectively. Therefore, the purpose of this research is to investigate the condition of onion farms in Fars province in terms of the amount of irrigation water and water productivity and the factors affecting them in agricultural conditions.

Yield, irrigation water volume and water productivity were measured and studied in 30 farms in three major onion production regions Shiraz, Sepidan and Kazerun in Fars province. T-test was used for statistical comparison of the obtained results. Three studied areas and three types of surface, sprinkler and drip irrigation systems were considered as experimental treatments. The daily meteorological information needed to estimate the water requirement was obtained from the synoptic weather stations of the regions using the Penman-Monteith method. Three scenarios were considered to estimate the water requirement of onion. 1- Water demand from the Penman-Monteith method, using the meteorological data of the year of conducting the research (2020) 2- Water demand from the Penman-Monteith method, using the meteorological data of 10 years leading up to the time of the research, 3- Water demand from the National Water Document. The average efficiency of surface, rain and drip irrigation systems was considered to be 60, 75 and 90%, respectively, and the gross water requirement was calculated. The farm water flow rates were measured with suitable instruments. The amount of irrigation water per hectare of the field was obtained by multiplying the amount of field discharge by the total duration of irrigation during the growing season. Finally, by summarizing the results obtained from the difference in yield values, irrigation water and water productivity, suggestions were made to improve onion production and irrigation.

The average volume of onion irrigation water in the three regions Shiraz, Sepidan and Kazerun was 15848, 14888 and 10507 m3/ha, respectively. The average total volume of irrigation water was 13880 m3/ha. The difference in irrigation water volume between Shiraz and Sepidan was not statistically significant at 5% level. However, the difference between the volume of irrigation water in Kazerun region and the other two regions was more than 4000 m3/ha, which became significant at the level of 1%. By applying 118, 327 and 195 mm of effective rainfall for the three investigated regions, the average amount of applied water in the fields of the three regions was 17028, 18158 and 12457 m3/ha respectively. The average volume of applied water in the three investigated areas was 16054 m3/ha. In terms of crop yield, the minimum, maximum, and average yield of the product in the three studied areas were about 34, 86, and 58 tons per hectare, respectively. The difference between Shiraz and Sepidan was less than one ton per hectare, which was not significant. However, the yield of Kazerun region was about 16 t/ha less than the yield of Sepidan and Shiraz regions, and these differences were significant at the level of 1%. The productivity of irrigation water in selected onion fields varied from 2.12 to 8.51 kg/m3 and on average was 28.4 kg/m3. The highest and the lowest irrigation water productivity were in Kazerun and Shiraz, respectively. The highest and lowest total water productivity was obtained in Shiraz and Sepidan regions. The difference in irrigation water productivity in the studied areas was not significant at the level of 5%. In Shiraz region, the amount of irrigation water given was 1003 and 723 m3/ha, respectively, less than the annual and long-term gross water requirements, and these differences were not significant at the 5% level. In Sepidan region, these differences were 1460 and 150 m3/ha, respectively, which was not statistically significant at the 5% level. In Kazerun region, the volume of irrigation water was 8427 and 7309 m3/ha, respectively, less than the gross water requirement, and these differences were significant at the 1% level. This means that the onion fields in this area did not receive the required water. In general, in the selected onion farms, the volume of irrigation water was less than the annual and long-term gross water requirement by 3419 and 2511 cubic meters per hectare, respectively, and these differences were significant at the 1% level.Comparison of irrigation water volume with gross water requirement showed that drip irrigation systems were most in line with gross water requirement and on average 5% more irrigation was done. However, in the fields under sprinkler and surface irrigation, 17 and 45% of water was given less than the gross water requirement, respectively. Therefore, the yield in onion fields under surface irrigation was about 28% lower than the yield in fields under drip irrigation system. The difference in irrigation water productivity between drip and surface irrigation systems was not significant.

In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R2) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.

 The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the natural resources. However, farmers and stakeholders need to be aware that through their management practices, they affect soil quality and, with the short-term goal of production and greater profitability, lead to soil degradation. They can both use the land economically and improve and maintain soil quality by balancing production inputs and refining their management approaches. There are different management systems of productivity in agricultural lands in Neyshabour plain in northeastern Iran. In addition to the water and soil limitations in the study area, the prevalence of the smallholder system and the unwillingness of farmers to integrate smallholder, has further increased the destruction of soils in the study area. The objective of this study was to assess the changes in soil quality index in surface soil and profile (0-100 cm) and calculate the correlation between soil quality index and alfalfa and rapeseed yield in rangeland and agricultural areas managed by smallholders, total owners, and Binalood Company in the study area.

 A total of 21 soil profiles were described in the total owner, smallholder and Binalood company management system and sampled from the alfalfa and rapeseed lands. Questionnaires were prepared with the help of farmers and experts in the study area based on Analytic Hierarchical analysis (AHP) method. The physical and chemical characteristics of the soil samples were determined. The important soil characteristics affecting plant growth were determined by interviewing farmers and experts study area. Soil quality index in the minimum data set (MDS) was calculated by two methods of principal component analysis (PCA) and expert opinion (EO), by additive and weighted methods in surface soil and profile. To achieve a single value for each soil properties in the soil profile, two methods of weighted mean and weighted factor were used. To evaluate the accuracy of the assessment, the correlation between soil quality index and alfalfa and rapeseed yield was investigated of the various management system.

The results showed that the highest additive and weighted soil quality index at both surface and soil profile in both PCA and EO methods were in rangeland. It was due to lack of cultivation and maintaining organic matter comparing to agricultural land. The total owner management system due to its economic power and the use of appropriate and scientific methods comparing to smallholder management system, showed the highest additive and weighted soil quality index. In all management system, the EO-calculated weight index by weighted factor method had the highest value due to assigning the suitable weight for soil characteristics. The correlation analyses soil quality indices with canola and alfalfa indicated that the EO soil quality calculated by weighted factor for the soil profile were more correlated than surface soil in total owner system and the Binalood company. Weight coefficient method due to the application of different weights to each layer based on their importance, showed a higher soil quality index in both EO and PCA sets than the weighted average method. The reason for better EO performance probably is that the PCA is a reducing the dimensions, meanwhile, the minimum data selection in the EO method is based on regional experts which are familiar with cause-and-effect relationship of the soil properties. Due to the relatively good correlation of the yield of the studied products, with the soil quality index, an appropriate management needs to maintain and improve soil quality, especially in the smallholder system, as well as meeting the nutritional needs of these products.

 Soil quality assessment in this study indicated that calculation of the soil quality index only considering the surface soil properties may not provide complete information for the farmers and land managers. Then inclusion of both surface and profile soil properties with farmers' knowledge and study area experts are essential for sustainable soil management. On the other hand, the differences in the management system also affected the soil quality index. Although the smallholder management system due to low input, especially chemical fertilizers, water and agricultural implements, had a high potential concerning environmental issues, but in terms of production, total owner and Binalood company management systems because of their high economic strength had the higher soil quality index. The farmers and stakeholders of the total owner management systems should be considered despite the proper management, however due to high inputs of fertilizer and water, especially in the Binalood company, the production may not be sustainable. Therefore, for further studies, calculating the water consumption in the desired management systems is recommended.

Optimizing the sustainable use of water resources across local or regional systems is a complicated management challenge with significant implications. The world population in 2050 will be more than 9.5 billion people that it needs about 60% more food. Furthermore, the water availability for irrigation will decrease because of competing demands from other sectors, i.e., industrial, domestic, and hydroelectric generation. Pre-growing season estimation of agricultural outcomes such as water losses, net benefit and yield production can provide insights to beneficiaries and farmers to make optimum decisions. Shahhosseini et al., (2019) evaluated the potential of machine learning algorithms for maize growth simulator to develop a decision support system. Three million data such as genotype, environment and management scenarios were incorporated for predicting yields in this framework. The results provided a dynamic decision technique for pre-season management. In a case study, maize water use was measured based on the water balance in a 6-year field trial in the west-central Great Plains of the United States by Trout and DeJonge (2018). Varzi et al., (2019) presented an optimization model to allocate irrigation water for maize using crop water production functions under deficit irrigation. The problem structure was determined based on maize and sunflower water productivity data collected during 2008–2011 growing seasons in eastern Colorado. The results show that deficit irrigation does improve farm income at moderate to high leasing prices. Moreover, the proposed model can define the crop and water leasing prices for which deficit irrigation is worthwhile. Therefore, increasing the efficiency of land and water resources can be considered as the main policy to find the sustainable decision. It is necessary to develop methods to improve soil moisture capacity and increase plant biomass in arid and semi-arid areas. This study was conducted to evaluate the impact of sugarcane compost on maize yield under the climate conditions of Gotvand plain in Khuzestan province, southwestern Iran. This agricultural residue can be used for improving the soil porous media structure to increase water availability in the root zone. Economic analysis of the application of sugarcane bagasse at different levels of plant water supply needs has been done using the development of the growth simulation model and optimization technique.

In this study, simulation of maize yield and soil water content with AquaCrop software under full and deficit irrigation managements and sugarcane bagasse utilization was evaluated. This agricultural residue can be used for improving the soil porous media structure to increase water availability in the root zone. Economic analysis of the application of sugarcane bagasse at different levels of plant water supply needs has been done using the development of the growth simulation model and optimization technique. A field experiment with three treatments of sugarcane compost application (0, 15, and 30 ton/ha) and four levels of crop water supply (50%, 75,% 100%, and 125% of total water requirement) was performed in three replications for two growing seasons (March to December 2019). Irrigation planning, cultivation costs, biomass, harvest index and canopy cover were measured at different time steps of the growing seasons to provide growth simulation models using AquaCrop software. The error statistics of calibration process were evaluated by root mean square error (RMSE), Nash– Sutcliffe efficiency index (NSE), normalized objective function (NOF), and mean absolute error (MAE) for the simulated and observed yield production values.

Calibration and verification process were carried out based on the two replications and one replication, respectively. The results presented in the scatter diagram and the coefficient of determination values close to one showed that the simulation model can be used to estimate the yield production in different scenarios of irrigation water distribution. The results showed that the use of sugarcane bagasse increases the soil water availability, harvest index and yield production at a confidence level of 1%. Optimal irrigation planning in this study was performed using the gray wolf optimization algorithm with the objective of maximizing crop yield. Optimal irrigation strategies reduce water losses, increase biomass and improve economic efficiency compared to existing conditions. Optimal planning based on daily information can determine the depth and timing of irrigation simultaneously. Increasing the water productivity and the net income for farmers and reducing the costs of bagasse application could be considered as positive implications of the developed simulation-optimization model. Future research could explore water management strategies on the farm by developing new goals for other crops or cultivation patterns.

Considering that wheat fields are mainly irrigated by surface (basin) and localized (drip) methods, this study aimed to investigate, compare and analyze the effects of total applied water and yield factors on wheat Water Use Efficiency (WUE) of each irrigation system in Yazd province. In this way, the priority of addressing each of these factors in the two irrigation systems can be determined. The results showed that in the surface irrigation method, reducing the volume of applied water has played a more important role in improving the WUE than increasing the yield. In other words, to increase the wheat WUE of surface methods, implementation of water use optimization solutions are more prioritized compared to crop yield enhancement practices. In contrast, in the case of the drip irrigation method, the crop yield enhancement solutions have played a more decisive role in improving wheat WUE. In these fields, wheat agronomic practices such as proper planting date and density, selecting suitable and high yielding varieties, proper irrigation cycle, proper fertilizer application, and pest and disease control have had a significant role in WUE of wheat, which requires high attention by the farmers to these issues. It was also observed that despite the possible reduction of applied water in drip irrigation compared to the surface method and consequent quantitative enhancement of WUE, but there may be the possibility of low and unacceptable wheat performance. This can cause economic inefficiency of the drip irrigation system for wheat. So, it is highly recommended to consider and implement the above-mentioned agronomic practices in wheat drip irrigated fields.

The growing population has increased the need for food day by day. Therefore, it is necessary to improve the yield of plants per unit area. The increase in crop yield is largely due to the use of chemical fertilizers. This has led to the improper use of fertilizers by farmers. So far, many studies have examined the effect of surface (SF) and drip fertigation (DF) on surface (SC) and drip (DC) irrigation along with traditional fertilization at different levels of fertilizer for crop yield index. But each of these studies has been done under different circumstances. Due to the dispersion of results and the lack of a general conclusion from the published results, meta-analysis is required as a structured method to achieve a single result in relation to the impact of different levels of fertigation on the mentioned indicators. In this study, by performing meta-analysis on 21 studies in the field of fertigation, the effect of different levels of fertilizer was investigated. The results of each of these studies were classified into subgroups of different levels of fertilizer, which include subgroups of more than 100%, 100%, 70 to 100% and 50 to 70% of the recommended plant fertilizer requirements. The highest increase in crop yield was obtained at 100% of the recommended dose and the effect size was 1.25, but there was no significant difference between the fertilizer levels of 100% and 70% to 100%. Based on these results, the most suitable fertilizer level is 70 to 100%. In this subgroup, 30% of fertilizer consumption can be reduced without a significant reduction in crop yield.

In this study, the cultivation pattern in Kerman province and Qaleganj County has been compared. While considering production of crops, the results of this study have been adjusted in order to prevent the export of virtual water by decreasing the cultivation of low yield and water intensive crops in the county. A comparison between the main crops of Qaleganj County and the same crops in Kerman province, based on cultivation area, production and the calculation of crop yield, blue and green virtual water, and water dependency index has been presented in his study. Among the studied crops, wheat has the highest area of cultivation in Kerman province (90145 hectares) and Qaleganj County (15159 hectares). The lowest areas of cultivation in Qaleganj County belong to potatoes (95 hectares) and onions (134 hectares) and in Kerman province belongs to onions (5738 hectares). Potato yield in Qaleganj County is 41 tons per hectare and its virtual water content is 0.321 cubic meters per kilogram, which has the lowest virtual water content among these products after onions. Due to the limited water resources in Qaleganj County and to reduce the pressure on groundwater resources, it is recommended to increase onion and potato cultivation in Qaleganj County and to limit the development of water intensive crops. Increase in Alfalfa cultivation in Qaleganj County is also recommended according to the crop yield (32.70 tons per hectares in Qaleganj County compared to 10.59 tons per hectares in Kerman province) if the livestock value chain is completed.

Crop models evaluationin agriculture has been done by researchers. It helps them to determine the most appropriate crop model for the planning and simulation of crop response in different areas. Using can lead time and cost saving, helping to evaluate the effects of different situations on the crops yield, biomass and water use efficiency (WUE). Given the importance of the subject, this study was conducted for the accuracy and efficiency evaluation of AqauCrop and SWAP under three irrigation types (D: sprinkler irrigation with saline water, F: sprinkler irrigation with saline and fresh water, and S: surface irrigation) and five water qualities (S1: 2.5, S2: 3.2, S3: 3.9, S4: 4.6 and S5: 5.1 dS.m-1). NRMSE results showed that the accuracy of AquaCrop for the simulation of yield, biomass and WUE was 0.07, 0.09 and 0.07, respectively. For SWAP, these were 0.12, 0.04 and 0.13, respectively. According to EF, AquaCrop results for above-mentioned parameters were 0.60, 0.90 and -4.4, and SWAP results were 0.74, 0.73 and -2.0, respectively. So, AquaCrop accuracy and efficiency were better than those of SWAP for the simulation of corn yield and biomass.

In order to investigate the effect of different intervals and irrigation methods on the growth characteristics of canola, an experiment was conducted in the form of split strip plots based on a randomized complete block design with three replications during 2009-2010 at Hajiabad Agricultural Research Center. In this regard, three irrigation intervals including irrigation after 20, 40 and 60 mm of evaporation from the surface of the evaporation pan (I1, I2 and I3, respectively) and two drip irrigation methods (surface and subsurface) were considered as the main and secondary factors, respectively. The results indicated the lowest and highest yield (1.97-3.15 t/ ha), 1000-seed weight (3.38-4.82 g) and number of seeds per pod (14.5-24.5) at the I1 and I3 irrigation intervals, respectively. However, the I2 irrigation interval treatment resulted in a 9.6% increase in water use efficiency compared to the I1 irrigation interval. Also, the use of subsurface drip irrigation compared to surface drip irrigation method increased crop yield, water use efficiency and number of pods per plant by 29.8%, 32.2% and 33.1%, respectively. The findings of this study showed that in general, in case of water insufficiency in the study area, the irrigation cycle after 40 mm of evaporation from the pan applied in the subsurface drip irrigation method can be suggested as a superior treatment. With this approach, we can take steps to deal with the water crisis based on sustainable agriculture in canola cultivation in Hajiabad region.

Soil fertility represents the soil ability to provide optimal plant growth conditions. Information on soil fertility status and preparation of related maps, plays an essential role in sustainable agricultural planning, maximizing economic benefits, maintaining soil quality, and preventing environmental degradation.In order to evaluate soil fertility status in some drylands in south of western Azerbaijan province and north of Kurdistan province, 101 samples were randomly collected and different soil properties such as pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), and total nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) were measured.Nutrient Index Value (NIV) was used to interpret the results and map soil fertility.The results showed that 100% of the soil EC and pH of areas were classified in the low and high classes, respectively.Organic carbon (OM) and CCE were in the medium class in 72% and 61% of the areas, respectively.Total Nitrogen in 94% of areas in medium class, available P in 94% of areas in low class, K in almost 100% in high class, available Fe in 88% of areas in low class, Zn in 33% in low class, 39% in adequate class, and 28% in high class, Cu and Mn available in 100% of areas are in low class.It can be concluded that NIV method is useful to evaluate soil fertility status and its predictions conform to the reported critical level for rainfed wheat.

Corn is one of the most important crops that is well adapted to arid and semi-arid regions. Amount and quality of irrigation water has an important effect on its yield. Therefore, different researchers have tried to study the effect of different irrigation methods and qualities on corn’s yield and biomass. The Iran\s arid and semi-arid climate confirms that it is necessary to study the best irrigation methods along with different qualities of irrigation water on the growth of this crop. In order to save time and money, crop models have been proposed to simulate the response of plants to different field conditions. AquaCrop, provided by the Food and Agriculture Organization (FAO), is one of the best crop models due to the simplicity, low input data, user-friendliness, high accuracy and acceptable proximity of modeling conditions. The present study was conducted using data collected by Minaei (2014) in a research farm in Ahvaz during two cropping seasons (2012-2013) on corn crop. In this research, irrigation types (D: sprinkler irrigation with saline water, F: sprinkler irrigation with saline and fresh water, and S: surface irrigation) and water quality (S1: 2.5, S2: 3.2, S3: 3.9, S4: 4.6 and S5: 5.1 dS.m-1) were examined. The minimum salinity was selected equal to the available water source as S1 treatment, which was the Karun River. On the other hand, the maximum salinity was also considered as S5, to reduce the corn yield by 50%. The S2, S3 and S4 treatments were also the intermediate between these two treatments. The highest and lowest differences between the simulated and observed yields were 0.7 and 0.1 ton.ha-1, obtained from SS5 and FS1 treatments, respectively. The average of these values was equal to 0.3 ton.ha-1. It can be concluded from the results that the model had an average proximate error of 300 kg. Based on the results, the maximum and minimum values of the biomass differences were 2 and 0.1 ton.ha-1, obtained from SS3 and FS3 treatments, respectively. The average difference was 0.7 ton.ha-1. Comparison of biomass results and yield showed that the differences in results between yields were less than those in biomass. If these results are expressed as a percentage; the highest and lowest differences between the observed and simulated yield values were 223 and 31%, respectively. The highest and lowest percentages of these differences in biomass were 252 and 12.6%, respectively. By increasing water salinity, the yield and biomass decreased linearly. These results were observed in all three irrigation treatments. The canopy cover results simulated by the AquaCrop confirm these results (Figure 3). As it can be seen in the figures, the increase in salinity caused a rapid decrease at the end of the growing season. The maximum and minimum differences between the simulated and observed water use efficiency were 0.08 and 0.001 kg.m-3, respectively. The average difference was 0.03 kg.m-3. Based on these results, the AquaCrop had an underestimation error in simulating corn yield in all three irrigation methods. The accuracy of this model was almost the same in all three irrigation methods and was in the excellent category. However, according to Table (5), the efficiency of this model based on EF statistics for each of the used irrigation methods was less than the determined value. The AquaCrop in simulating sprinkler irrigation with saline water (D) had an underestimation error to simulate corn biomass. This model had an overestimation error in simulating corn biomass in the two F and S irrigation methods. The accuracy of this model in simulating corn biomass in S method was less than sprinkler irrigation. The efficiency of AquaCrop in biomass simulation was desirable. The accuracy and efficiency of this model in simulating the water use efficiency of corn in all three irrigation methods were almost the same and acceptable. Comparison of two NRMSE statistics for two parameters of performance and water use efficiency represented that the model was more accurate in simulating performance. According to the results, AquaCrop had acceptable efficiencies for simulation of yield, biomass and soil salinity. However, its efficiency for water use efficiency was not acceptable. In addition, the AquaCrop’s results were the same for all three irrigation types. Then, irrigation type had not any effect on AquaCrop’s accuracy and efficiency.

Optimal and efficient application of water resources due to the recent water crisis and droughts in the country is very necessary. In this regard, the use of modern irrigation methods is one of the solutions that has been developed to increase water productivity in the country. Therefore, this study was conducted to compare the physical and economical efficiency of water in drip and surface irrigation systems and net benefit per cost in vineyards and walnut orchards. In this study, the volume of applied water was calculated by measuring the flow rate of the water source, the number of irrigations and the hours of irrigation in each irrigation. The water requirement was calculated based on the last 10 years of meteorological data and compared with the estimated water requirement in the national water document. Water productivity was estimated based on yield per unit volume of water (CPD), income per unit volume of water (BPD), and net yield per unit volume of water (NBPD). The results showed that drip irrigation method compared to surface irrigation method reduced the applied water of grape and walnut crops by 40 and 54%, respectively. Water productivity in vineyards and walnut orchards in drip irrigation system was calculated as 25.1 and 1.81 and in surface irrigation as 7.8 and 0.88 kg / m3, respectively. Calculated water requirement based on the last ten years of meteorological data in these two products was greater than the value stated in the National Water Document. Comparison of the average amount of applied water with the calculated water requirement indicated a deficit irrigation in most of the studied gardens. Comparison of Economic productivity of water in the two crops showed that this index in drip irrigation method is much higher than surface irrigation method. Net yield index per unit of water used (NBPD) in drip irrigation method in grape and walnut crops are 231 and 117, respectively, and in surface irrigation method 110 and 115 thousand Rials per cubic meter.

Sustainable agriculture will be achieved only when land is used, essentially based on soil and water capacity. Otherwise, agricultural lands will influenced by increasing degradation and erosion. Therefore, this study aimed to evaluate the land suitability of orchard (200 hectares) located on the southern Urmia plain (Darbrud soil series) and also provide a suitable irrigation model using CROPWAT considering irrigation water shortage and lack of adequate drainage for apples as a main crop in this area (%55). Ten years of climate data was collected, and the land index was evaluated by parametric method (square root method). Limitation in drainage status resulted in (S3w) for land subclass, while for climate and temperature suitability, the minimum average temperature during the month before harvest, reduced the subclass to S2. Moreover, CROPWAT model was used to estimate the efficiency of model for important horticultural crops under a proper-irrigation management in the study area through comparing the results of the model with those from the FAO model. The result of this study indicates that an excessive or over irrigation regime has occurred in the studied fields which have no positive effect on the crop yield or economic benefits. Providing irrigation projects based on a suitable water model will increase the water use efficiency.

Estimating actual evapotranspiration in river basins is necessary to use water resources optimally and to improve river basin management. SWAT hydrologic model and SEBAL remote sensing algorithm are among the known methods which have addressed this issue. In the present study, in the first step, the actual evapotranspiration of Karkheh river basin was estimated in dry, normal, and wet years (2008, 2012, and 2015, respectively), using the SWAT model calibrated based on runoff and crop yield and SEBAL algorithm. SWAT model was calibrated and validated using six hydrometric stations for the periods of 1993-2009 and 2010-2013, respectively, in which the , NS and RMSE values were ​​obtained between 0.45 to 0.7, 0.52 to 0.67 and 12.64 to 25.02 (m3/s) for the calibration period and between 0.4 to 0.6, 0.3 to 0.56 and 11.08 to 23.17 (m3/s) for the validation period, respectively. Further, the average observed and simulated yield of the strategic crop (wheat) in the basin were equal to 4.70 and 5.01 (ton/ha), respectively. In addition, the results of SEBAL algorithm and SWAT model were compared together based on the water year status, which the correlations between the results of those methods were equal to 0.74, 0.60, and 0.52 for normal, dry, and wet years, respectively. In the second step, based on the ground data and MODIS, which has a suitable temporal resolution, and OLI which has a suitable spatial resolution, the results of SEBAL algorithm and the variation ranges of main parameters are presented for Pole-dokhtar and Ravansar plains. Ravansar plain has more cultivation areas and lower topography changes compared to Pole-dokhtar plain. The simulation of crop yield by SWAT gave a better result in Pole-dokhtar plain. Based on the results of this study, the values ​​of evapotranspiration obtained from SEBAL algorithm and SWAT model can be reliable and close to the actual values ​​of evapotranspiration in the river basin.