سینا ملاح

In the first part of this paper, multi-dimensional understanding of the soil and water management of agricultural lands of Honam Sub-catchment was provided by determining the 53 homogeneous soil and water management units (HSWMUs), current behavior assessment of the farmers, and the yield gap analysis. It was found that efficiency of water and fertilizer consumption in the region was relatively low. Therefore, soil and water management packages including soil fertility management, irrigation management and land degradation prevention were prepared for each HSWMUs. The method of preparation and validation steps of management packages is the subject of this paper. To transfer the achieved knowledge and technology and to evaluate and validate the recommended packages, Aliabad Agricultural Jihad Center of Lorestan was selected as an innovation platform. Scaling-up was carried out by active cooperation of multi-level local to national experts to hold a technology exhibition including management of optimal use of chemical, organic and biological fertilizers, and irrigation methods to test the research results. To this end, the selected technologies and recommendations were implemented at volunteer farmers’ fields. The pilots were separated into two treatments of farmers' conventional practice and integrated recommendation in the beet-wheat rotation. The results of the best bet showed that, on average, crop production of winter wheat increased from 5 to 7.5 t/ha and water use efficiency improved by 13%. These results were displayed to other farmers on the field day, which matched up with the time of wheat harvest. By proofing the effectiveness of the results in the farmers' fields, the soil and water management packages of the region were prepared. Despite holding workshops for farmers, results were not scaled to others. We found that the out-scaling of soil and water management packages within HSWMUs and improvement of the average yield along with the principles of sustainability is rooted in three gaps: yield, research and development, and value chain gap. In other words, the awareness and motivation of the farmers, the ability to implement recommendations, and the availability of facilities are the basic conditions for filling crop yield gap in the region. Therefore, one-year pilots are not effective enough and should be continued for several years. Afterward, the capacity building of local extensions at the Soil and Water Research Institute (SWRI) was carried out to show the different stages of preparing HSWMUs, recommendations and instructions. All data were transferred to the innovation platform in digital and printed format. Receiving feedback and to continue scaling-out under spiral proses method, a number of farmers of HSWMUs who volunteered to test the recommended packages in their farms were selected, and the pilots was designed based on participatory research. It is expected that with the establishment of long-term demonstration farms and the provision of various requirements such as the existence of various fertilizers and other agricultural inputs by the collaboration of public and private sectors, the transfer of recommendations to other farmers as well as decision makers of the sub-basin is facilitated.

This study expresses the steps of achievement to the indigenous-executive framework of integrated soil and water management at farm level through a mega project with 19 sub-projects. The projects were carried out during 2013-2020 with active participation of international, national and regional experts as well as local farmers in Honam Sub-basin, Lorestan Province, Iran. To understand the soil and water management problems for agriculture in the same homogeneous units (HSWMUs), farmlands were classified regarding physical, chemical and biological characteristics of soil, making it possible to convert soil maps into thematic maps. In this research, the fertility capability classification (FCC) method that integrates surface and sub-surface soil textures with modifieres was used. In addition, several modifieres were revised for soils of semi-arid regions and new modifieres were introduced for Irrigation Capability Classification (ICC). The required information was collected from 119 soil profiles and 101 surface soil samples in detailed scale of 1:25000 and the corresponding measurements were made. Accordingly, different data layers including detailed soil map were produced via geopedological method icluding land limitation, land suitability, and land production potential (LPP) of major crops, land degradation, location of water withdrawal, land use and agricultural cadastre of 2395 farms were prepared. To evaluate the current soil and water behavior of farmers within HSWMUs, the multidisciplinary team recorded the issues through field visits and open semi-structured questionnaire of 84 selected farmers in 24 villages who were interviewed during 2018-2019. The results showed that 23 and 30 HSWMUs that covered, respectively, 93% and 90% of the irrigated and rainfed areas were delineated on the map. Investigations showed that the sub-basin suffers from problems such as plow pan, phosphorus and zinc deficiencies, poor irrigation scheduling, and three types of farmers. Finally, recommended packages were prepared and validated for each HSWMU, which will be discussed in the part 2.

For sustainable water resources utilization, knowledge of the existing conditions as well as a proper method to allocate and exploit water is inevitable. To this end, water resources and cropping pattern of Honam sub-catchment in Karkheh River Basin, Iran, were geographically identified and then digitized over 913.8 ha of irrigated lands, during 2017-2018 growing season using field survey and satellite imagery. Also, irrigation homogenous units were separated and hydro-module of cropping pattern was determined per unit according to surface irrigation application efficiency of the study area. The results of the cropping pattern showed that the areas under the major crops including wheat and barley, alfalfa and clover, and sugar beet were 444, 153, and 40 ha, respectively. Alfalfa and barely had the highest and lowest irrigation requirement among the major crops with 11910 and 3000 m3.ha-1, respectively. Nineteen irrigation homogenous units were delineated, and their mean annual hydro-module was determined as 1.26 L.s-1.ha-1. Meanwhile, mean hydro-module of nineteen homogenous units was obtained as 0.86 L.s-1.ha-1 according toestimated volume of water and average of application efficiency (30%) in the study area. The results also indicated that the allocated water for 12 out of 19 homogenous units was more than the determined hydro-module, and for the remaining 7 units it was less. It can be concluded that it is feasible to reduce drought stress and improve yield and water productivity based on determined hydro module and water consumption volume of subcatchment cropping pattern.

Agricultural water usage efficiency is one of the important goals of water productivity improvement program of Jahad-e Agriculture Ministry in Iran. In this case, the determination of appropriate irrigation scheduling, which is consisted of irrigation depth and frequency, is a key strategy. Many of the country’s agricultural plains are under traditional irrigation scheme and unlike modern irrigation networks have a more complex consumption pattern. This study was carried out to determine the irrigation depth and scheduling of major orchards of Honam sub-catchment of Karkheh River Basin (KRB), in order to optimize agricultural water consumption at basin level. Honam sub-catchment was selected as the research pilots of KRB. Nineteen irrigation homogenous units supplied from joint water resources were delineated using ArcGIS 10.3 to predict irrigation depth and frequency of cropping pattern in 913.8 ha irrigated lands of study area. In addition, the land use map of crops and orchards in the irrigated lands was prepared by field survey and combining satellite images of irrigation channel, cultivation type and their distribution. The 15-years (2000 – 2014) data of Alashtar weather station were collected and the reference evapotranspiration (ET0) was calculated based on Penman-Montith method using ET Calculator software. Moreover, the length of plant growth period of cultivation pattern was determined by completing a questionnaire. Then, evapotranspiration of cropping pattern (ETc) was calculated considering proposed Kc of FAO-56. Effective rainfall was deducted from ETc and finally, irrigation requirement of crops and orchards were calculated by subtraction of ETc and effective rain as ten-day, monthly and entire growth period. The related soil physical properties for irrigation purposes including soil texture, Field Capacity (FC), Permanent Wilting Point (PWP) and water holding capacity, were measured at different soil depth at each irrigation homogenous units. The net irrigation requirement of winter wheat in Honam sub-basin was calculated to be 318.1 mm. The maximum amount of net irrigation required for wheat was 112 and 5.7 mm in May and March, respectively. The annual and monthly maximum and minimum irrigation requirements for sugar beet were 842.3, 204.4 and 19 mm, respectively. For mixed orchards, the annual irrigation requirement was 993.4 mm, and the monthly maximum and minimum irrigation requirements were 230.2 and 14.8 mm, in August and April, respectively. For alfalfa, the annual irrigation requirement was 1190.7 mm, and the monthly maximum and minimum irrigation requirements were 284.4 and 7 mm, in July and March, respectively. Among the crops, the maximum irrigation requirement was related to alfalfa and then mixed orchard, and the minimum irrigation requirement was related to winter wheat with 318.1 mm during the growing season. The results also showed that the most of the irrigation homogenous units soil texture were silty clay loam and clay. Due to the favorable rainfall in autumn, winter and April, the plant's water requirements are met by rainfall. Therefore, no irrigation is required except in May and June. Wheat irrigation period in May was calculated as 16 days on average among all water units, and 11 days in June. In June, when the maximum water requirement of wheat occurs, the maximum irrigation interval for wheat is 13 days in all-homogenous units of 4 and 14 and the minimum irrigation interval is 10 days in all-homogenous units of 6, 10, 12 and 16. The irrigation interval for sugar beet in May was 21 days on average among the same homogenous units and in June. The irrigation intervals for sugar beet in July, August, September and October were 11, 7, 8 and 11 days, respectively. In July, when the maximum water requirement of sugar beet occurs, the maximum irrigation interval was 9 days in homogenous irrigation units of 14 and 15, and the minimum irrigation interval was obtained 7 days in irrigation units of 6, 10, 12 and 16. The irrigation periods for orchards and horticultures among all units in June, July, August, September and October were 17, 10, 10, 12 and 19 days on average, respectively. Alfalfa irrigation interval is shown in the units of 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, and 19 of irrigated lands of Honam sub-catchment. Meanwhile, its irrigation intervals among all irrigated units in April, May, June, July, August and September were 29, 21, 10, 7, 7 and 10 days on average, respectively.

Suitable water management in irrigation and drainage networks without mapping water requirement of different plants in macro levels is not possible. Iran was located in arid climate and has Drought problems and management of Drought, need to macro management of its water resources. In this study using meteorological data in Khuzestan, reference evapotranspiration and Wheat water requirement was determined in pilots. Finally different methods of mapping for example regression method, Inverse Distance Weighted and Co- Kridging were evaluated. In calibration stage power and logarithmic regression presented suitable results and have 8 and 16percent error respectively. Result show that ET0 and water requirement have significant relation with height. So these parameters have spatial continuity. In evaluating stage,  The result show that Co- Kridging method with  0.82 R2 (regression coefficient) , 0.66 d(Agreement index)  , 22.9 Root Mean Square Error  and 0.05 Normal Root Mean Square Error  and  5 percent Normal error  has lowest error in estimation of water requirement  of wheat and using this method can mapping water requirement with well accuracy. So Co- Kridging method is the best method between other methods. Use map obtained can has suitable conclusion from wheat water requirement changes in Khuzestan and manage crop pattern base of maps.

In order to increase water productivity at farm level and smart irrigation, appropriate systems are essential so that can measure the moisture content in a proper and acceptable accuracy. In this regard, the wetting front device was made at the Soil and Water Research Institute and was evaluated in 3 textures: light, medium, and heavy at 3 levels of water salinity: no salinity (S0), 5(S1), and 10 (S2) dS/m. Then, to evaluate and measure the device in the mentioned conditions, it was tried to simultaneously measure the device numbers and soil sampling at certain depths at intervals of 24 h. After collecting data on the time of arrival of the moisture front to the soil depth and changes in soil moisture, statistical analysis was performed by soil sampling and soil detection device. The results showed that the device reacts in different textures and salinities. Moreover, the sensitivity of its sensors to sudden changes in soil moisture due to the arrival of the moisture front to a certain depth of soil has acceptable accuracy. Statistical results showed that the device has about 6 to 9% normal error in determining soil moisture in non-saline conditions, 28 to 41% in terms of using water with salinity of 5 dS/m, and 31-37% when a water with salinity of 10 dS/m was used. The model efficiency index also showed that the device is very useful in non-saline conditions with an average efficiency of 0.75 and is not recommended in saline conditions with negative efficiency index.

Since synoptic metrological stations have non-uniformed scattering pattern in Iran and on the other hand precipitation determination and forecasting is essential for irrigation planning, a method precisely determine precipitation of agricultural lands in farm level has great importance. This study was carried out in Google Earth Engine Code programming environment using GPM, TRMM and CHIRPS satellite data which is daily, decade and monthly, respectively in Ahwaz and Izeh metrological stations for calibration and 9 meteorological stations for validation during 2015-2016 and 2017 - 2018 Growing season. Results showed that monthly interval could obtain better accuracy with R2 of 0.99 and NRMSE = 0.36, respectively. The validation results of the rest 9 meteoroidal station indicated that precipitation prediction had 51% and 3.1 mm error and under estimation on average, respectively. The efficiency was reasonable and F-Test showed no significant difference between observed and prediction samples. The standard error value was 14.2 mm which is a significant error and need to work on updated better functions. It can be concluded that this method can be a useful tool for monthly precipitation prediction of areas with no climatic data if integrated with Kriging, co-Kriging and Inverse Distance Weighted (IDW) geostatistical models for interpolation.

Soil texture is a static soil property that has great effects on soil physico-chemical properties. Therefore, global demands are increasing for a high spatial resolution map of soil texture. Lack of intrinsic soil data can lead to wrong policies regarding management and degradation of soil and water resources. Iran has many scattered soil data that have been collected at great cost. These data can be useful in a wide range of applications if presented accurately in digital map format. In this study, Ordinary Kriging, Pixel-Based Classification (PBC), and Inverse Distance Weighted (IDW) methods were investigated using 4665 soil surface samples collected from croplands and orchards to map Guilan soil texture groups (fine, medium and coarse) and soil mineral particles. MBE, NRMSE, KIA, R2 and Pa statistics were used for verification. The results indicated that IDW could provide higher accuracy for clay (R2 = 0.64 and NRMSE = 0.22) and sand (R2 = 0.67 and NRMSE = 0.25) particles prediction, but PBC had higher accuracy for predicting fine, medium and coarse soil texture groups according to KIA and Pa of 0.46 and 0.73, respectively. However, superiority of PBC was minor (KIA = 0.43 and Pa = 0.71) compared to Ordinary Kriging. PBC used auxiliary soil data as inputs for Artificial Neural Network to predict soil mineral particles of unvisited pixels. For more certainty regarding efficiency of PBC in predicting soil texture groups, it is recommended to test the mentioned methods in areas with more physiographic diversity.