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Irrigation scheduling: a soft adaptor to weather uncertainties and irrigation efficiency improvement initiatives

KC, Birendra
Fields of Research
ANZSRC::0905 Civil Engineering , ANZSRC::090509 Water Resources Engineering , ANZSRC::070108 Sustainable Agricultural Development
Expanding dairy farming around the world including the Canterbury region of New Zealand is causing increasing demand for irrigation, placing more pressure on already stressed water resources. The challenge for New Zealand dairy farming is to maintain an appropriate equilibrium between pasture production and environment protection, achievable through the proper management/utilization of agricultural water, for which application and expansion of carefully identified and evaluated irrigation scheduling can play a key role. The focus of this research was, therefore, to contribute to the development of irrigation scheduling to determine the irrigation range within the soil water holding capacity taking into consideration precipitation (P), evapotranspiration (ET), plant available water (PAW) and crop coefficient (Kc). This was achieved through estimating Kc of pasture at different grazing rotations by field measurements and analysing irrigation and deep percolation under a range of PAW-based irrigation triggers by applications of mathematical modelling of irrigation scheduling. A farmers’ survey has been carried out with 32 dairy farmers in Canterbury, New Zealand to collect information on current irrigation practices, particularly in relation to PAW and grazing rotation. The experiments were conducted at Lincoln University Dairy Fram (LUDF), South Island, New Zealand during the period August 2014 to March 2016. A network of 20 non-weighing lysimeters and an Aquaflex installed on LUDF were utilized for the study. Pasture height, precipitation, irrigation application, deep percolation and change in soil moisture in the lysimeters were measured throughout the study period. Time Domain Reflectometry (TDR) probes with 200, 500 and 900 mm lengths were installed vertically adjacent to the Aquaflex and lysimeters for improving soil moisture determination in the lysimeters without disturbing natural water flux inside the lysimeters. To account for climatic variability, available 16 years of climatic data were collected from Broadfield weather station. Irrigation and deep percolation have been estimated using two soil-plant-atmosphere mathematical models (IrriCalc and CropWat 8) under a range of irrigation management strategies, including those identified in the farmers’ survey, and commonly applied crop coefficient values in addition to those estimated in this research. Based on reference and actual evapotranspiration, Kc of pasture was estimated for different grazing rotations. Analysing the relationship between Kc and crop canopy represented by pasture’s height (h in cm) showed that a linear fit simulates well this process. Aquaflex soil moisture (SM) readings resulted in a value of 0.43 for the coefficient of determination (R2) for the Kc – h relationship, which increased to 0.66 when Aquaflex SM measurements were adjusted for each lysimeter using corresponding TDR readings. This signifies the importance of accurate soil moisture determination to improve irrigation planning. The estimated values of Kc just after and before grazing were 0.6 and 1.0 for corresponding pasture heights of 10 cm and 30 cm. Average Kc for one grazing rotation was estimated at 0.7. This implies conventional irrigation planning with a constant pasture crop coefficient of 1.0 would provide “on average” 30% more water compared to the actual water demand of pasture under grazing condition. This significant amount of water saving can contribute to conserve water and reduce leaching of nutrients. During the shoulder seasons (September – October and March – May) current irrigation strategy leaves sufficient space for potential rain. However, during the peak irrigation season (November - February), the majority of farmers apply irrigation to fill soil up to 100% of the Field Capacity (FC), which is prone to cause deep percolation if rainfall follows an irrigation event. Analysis of the irrigation and deep percolation predicted for 14 irrigation seasons indicated that a minimum soil moisture level to start irrigation at 55 and 60 % of PAW, respectively on the shoulder and peak irrigation seasons, and stopping irrigation correspondingly at 80 and 90 % of PAW were optimal for this case study. This would allow for rainfall harvesting and thus, reduce net irrigation requirement and deep percolation losses. These results will make important contributions towards improving irrigation scheduling. Such irrigation scheduling can serve as soft adaptor to cope with weather uncertainty. The proposed irrigation scheduling contributes to agricultural water management, eventually supporting the sustainable development of dairy farming industries in New Zealand and around the world. In addition, it would also decrease water pollution by reducing nutrient leaching from pastoral farms to water resources.
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