A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I₁: −20 kPa; I₂: −30 kPa; I₃: −35 kPa; and I₄: −40 kPa), while the sub-plots consisted of three fertigation levels (F₁: 120%, F₂: 100% and F₃: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols.

This study evaluates the performance of 50 empirical models for estimating potential evapotranspiration (PET) using daily meteorological data from a semi-arid region in Tamil Nadu, India. These temperature- and humidity-based models were compared against the FAO56 Penman–Monteith (PM) model, a globally accepted benchmark. Statistical indices such as the coefficient of determination (R²), mean absolute error, standard error estimate, and long-term average ratio (rt) were employed to assess model accuracy and reliability. The results revealed that certain models—specifically Althoff (Water 11:2272, 2019), Pereira (Agric Water Manag 66:251–257), and Samani (J Irrig Drain Eng 126:265–267, 2000)—exhibited strong agreement with the FAO56 PM model, offering a robust balance between accuracy and simplicity. The Althoff et al. model achieved the highest ranking based on standardized performance indices. This study is significant in identifying cost-effective alternatives for PET estimation in data-scarce, semi-arid regions, which is essential for irrigation planning and water resource management. However, a key limitation of this research is its exclusion of radiation and wind-based models, which may provide enhanced accuracy under certain conditions. Future research should explore the integration and calibration of multiple climatic parameters to improve PET estimation further.

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I₁:20%, I₂:30%, I₃:40% and I₄:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F₁:100%, F₂:80% and F₃:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I₁, followed by I₂, I₃ and I₄, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m³) and plant growth parameters under semiarid inceptisols.

Based on a field survey conducted in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, details about the crops grown, cropping pattern, land utilization and soil samples were collected from 20 locations under seven different soil textures of semi-arid vertisols. Using the data, efficient geostatistical models have been explored to study the spatial variation of irrigation water requirement of paddy, sugarcane and banana grown in the study area. Geostatistics was applied to identify the best interpolation method to acquire the spatial map of the available water holding capacity. Ordinary kriging and Disjunctive kriging were explored with six models viz., Circular, Spherical, Exponential, Gaussian, Penta-spherical and Sine-Hole effect models and were used for spatial prediction. The cross-validation statistics indicated that the Sine-Hole effect model with disjunctive kriging of available water holding capacity was superior for interpolation with a minimum value of RMSE and moderate spatial dependency. With the available water holding capacity map of Lalgudi block, total available water, readily available water and irrigation interval of paddy, sugarcane and banana were estimated. The irrigation interval of paddy, sugarcane and banana were found to vary between 1-2, 3-5 and 2-4 days respectively within Lalgudi block. The adoption of spatial algorithms for estimating crop water requirements would greatly help irrigation planners and water policymakers create efficient regional plans for precision irrigation under semi-arid vertisols.

Background: Water Footprint is a recently used indicator which helps to reduce water depletion and alleviate water stress in areas of drought and proper crop cultivation. Hence a study was taken up to assess the crop water footprint of different groundnut varieties namely TMV 7, VRI 2, VRI 3, VRI Gn 5, VRI Gn 6, CO 3, CO Gn 4, ALR 3 and TMV Gn 13 cultivated during Kharif and Rabi seasons at Tiruchirapalli district of Tamil Nadu. Methods: The total water requirement, blue and green crop evapotranspiration, blue and green crop water use and total water footprint for different varieties of groundnut were estimated using CROPWAT 8.0 Windows. A comparison was made between the water footprint of groundnut varieties and the strategies to reduce water footprint is presented. Result: The total water footprint for groundnut varieties ranged from 2603 to 4889 m3 ton-1 (CV of 26%) during kharif season, while it was ranged from 1465 to 2470 m3 ton-1 (CV of 18%) during rabi season. It was found that in all groundnut varieties the blue water footprint is higher than the green water footprint, while VRI Gn 5 variety had minimum total water footprint. It was concluded that, the groundnut production is affected by different levels of blue water stress which requires effective irrigation practices and water management strategies to enhance the crop production.

The performance of sixteen Valiantzas’ reference evapotranspiration models was investigated using the meteorological data obtained from Agricultural Engineering College and Research Institute, Kumulur, Lalgudi Taluk of Tiruchirapalli district, which is a semi-arid region located in Tamil Nadu, India. The Valiantzas’ reference evapotranspiration was compared with the globally used FAO56 Penman-Monteith method. The indexes used for comparison are coefficient of determination (R2), Standard Error Estimate (SEE) and long-term average ratio (RT). The Valiantzas’ models requiring complete dataset performed excellently in this station. The models not requiring wind speed data also performed equally well in this station and exhibited a fairly good correlation with FAO56-PM method. The other formulae accounting for local average wind conditions, reduced set formulae with temperature and relative humidity data alone and reduced set formulae with temperature and radiation data alone also performed well in this station. The investigation showed fair accuracy of Valiantzas’ Models and hence researchers can use these models in the absence of availability of the complete dataset.