सिंचाई एवं जल निकासी प्रणाली इंजीनियरिंग

Using agricultural water wisely in irrigated fields is very important, especially with water scarcity in arid and semiarid countries globally. An accurate irrigation water requirement calculation is required to determine real time irrigation scheduling, in order to apply the specific amount of irrigation water at the right time, and avoid crop growth stress which leads to reduced crop production. The main objective of this paper is to develop a mathematical model to accurately calculate daily Reference Evapotranspiration (ETo) as a first step for the accurate calculation of irrigation water requirements. Also, the model output was compared to ETo estimated using CROPWAT, an irrigation software program used for ETo calculation and irrigation scheduling. The reference evapotranspiration model was built using the Food and Agricultural Organization FAO-56 Penman-Monteith equation with the SIMULINK tool in MATLAB software. The model was validated by comparing daily estimates of evapotranspiration with Class A pan and evapotranspiration gauges in the United States. The results indicated a good fit between daily ETo calculated by the model and that observed from Class A pan and evapotranspiration gauge. There were some discrepancies between measured, modeled and CROPWAT ETo. This model is the first step to calculate accurate irrigation water requirements.

Irrigation development in semi-arid regions provides benefits for producers as well as many others who reside in the region. Although a common perception exists that irrigation benefits only irrigation producers, a study carried out by Paterson Earth and Water Consulting Ltd showed that irrigation positively impacts many more sectors of the Alberta economy than just irrigation producers. Compared to dry land (rain fed) agriculture, irrigation creates increased employment and economic activity through the purchase of additional farm inputs as well as processing of agricultural products. Multi-use water storage reservoirs, which support irrigation agriculture, provide societal benefits through recreation, hydropower generation, and water supply for habitat development, communities and industries. This study estimated that Alberta’s irrigation industry, which represents less than 5% of the cultivated land base, generates about $3.6 billion to the provincial gross domestic product (GDP), accounting for about 20% of the total agri-food sector GDP. It is also responsible for generating about $2.4 billion in income and creating about 56,000 jobs. Many of these jobs and incomes are generated in the rural regions of the province, and serves as an important part of the rural development initiatives in Alberta. Almost 90% of the GDP generated by the irrigation sector accrued to the region and the province, and only 10% accrued to irrigation producers.

Qualitative assessment of model performance is essential because reliable statistical comparison of observed data with simulated behaviour of a model reflects the performance and consistency of the mathematical tool under defined conditions. In this study we compared the measured temporal and spatial distribution of water content, soil solution salinity (ECsw), and nitrate (NO3 --N) concentration in the soil beneath a drip-fertigated mandarin tree during a complete season with corresponding HYDRUS-2D simulated values using a range of standard statistical techniques, comprising mean error (ME), mean absolute error (MAE), root mean square error (RMSE), paired t-test (tcal), coefficient of determination (R2), Nash and Sutcliffe model efficiency (E), index of agreement (IA), relative model efficiency (Erel), relative index of agreement (IArel), modified E (E1) and IA (IA1). Temporal and spatial values of ME, MAE, and RMSE for water content (-0.04 to 0.05 cm3.cm-3) and salinity (-0.42- 0.93 dSm-1) were within an acceptable range. However, a relatively wider range in MAE (1.44-27.65 mg.L-1) and RMSE (2.00-39.57 mg.L-1) values were obtained for NO3 --N concentrations measured weekly or at the 25-cm depth (MAE = 21.2 and RMSE = 30.7 mg.L-1). Temporal and spatial RMSE were higher than MAE, which suggests a slight bias in RMSE due to squared differences between measured and simulated values. Similarly, the paired t-test (tcal) showed significant differences for NO3 --N during the mid-season (85-140 DOY) for temporal (weekly) comparison and at several depths for water content (10, 25, 80 and 110 cm), salinity (100 and 150 cm) and NO3 --N concentration (25, 100 and 150 cm). The R2 values varied in a narrow range (0.5 to 0.59). Similarly, values for E (0.12-0.43), IA (0.80-0.84), and E1 (0.26- 0.32) and IA1 (0.61-0.69) suggest that the model precisely predicted water content, salinity and nitrate concentration over the season, however, Erel (-319.25) and IArel (-71.3) values were highly negative for nitrate concentration, indicating a mismatch. It was concluded that none of the evaluated measures described and tested the performance of the model for water, salinity and nitrate ideally. Each criterion had its specific advantages and disadvantages, which should be taken into account. Hence, sound model performance evaluation requires the use of a combination of different statistical criteria, which consider both absolute and relative errors. Judicious use of statistical criteria should lead to improvements in the modelling assessment of water, salinity and nitrate dynamics in soil under cropped conditions.

Land and water are finite natural resources, which are diminishing due to indiscriminate and unscrupulous exploitation. Ever increasing population is also posing a challenge to produce more from the available resources. Proper understanding of the concept of land, water and energy productivity has become quite relevant in recent days. The technologies/ strategies in which input application is more precise, efficient and cost effective and output is adequate in quantity, excellent in quantity, well in time and profitable, will lead to enhancement of land, water and energy productivity in agriculture. In this paper, concept of land, water and energy productivity, assessment procedure of crop and agricultural water productivity and case study of Pabnawa minor in Kurukshetra, Haryana (India), have been discussed and various means/ pathways to enhance productivity by employing suitable technologies/strategies have been highlighted.

A study was carried out to determine the crop water requirement of few selected crops for the command area in Tarikere taluk in Karnataka state, India. The crops include areca nut, coconut, and cotton, banana for two seasons, sweet pepper, onion, potato, rice, pulses, mango, and cotton, sugarcane and millet (ragi). Crop water requirement for each crop was determined by using 30-year climatic data in CROPWAT. Reference crop evapotranspiration (ET0) was determined using the FAO Penman Monteith method. For all the crops considered, three decades: decades I, II, and III and seven crop growth stages: nursery, nursery/land preparation, land preparation, initial stage, development stage, mid-season and late season stage were considered. The study shows that reference evapotranspiration (ET0) varies from 2.5 to 3.36 mm/day for the area under study. The gross water requirement was 342.42 mm/year with an application efficiency of 70% and hence the entire crop area of 4466 ha requires 16 MCM. Thus the dam can conveniently supply the water required for irrigation in the area.