Performance evaluation and optimization studies of border irrigation system for wheat in the Indian Punjab

Surface irrigation methods are the most widely practiced worldwide for irrigation of row crops. The major problem with these methods is low irrigation efficiency, mainly due to poor design. In the Punjab, border irrigation is used to irrigate wheat crops grown over 90% of the cultivated area. The evaluation of existing border systems using a surface irrigation model showed that the irrigation conditions, comprising of inflow rate, border dimensions, and cut-off time, were diverse in tubewell and canal irrigated areas. The study also examined the feasibility of optimizing border dimensions taking into consideration the existing irrigation conditions for achieving more than 60% application efficiency as compared to the 30–40% achieved under present field conditions. In the case of a border length of 60 m, it was recommended to increase border width in the range of 10–45 m and 20–60 m for different flow rates of 10, 20 and 30 L/s in light and medium soils, respectively. For higher flow rates, a border length ranging from 120–150 m was found to be optimum. For a border length of 150 m, it was recommended to keep a border width ranging from 4–38 m and 8–65 m in light soils and medium soils, respectively, for flow rates of 10, 20, 30 and 60 L/s. Optimizing border dimensions is a practical way to achieve efficient and judicious use of water resources

Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a Random Forest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves–Samani, Modified Penman and modified Hargreaves–Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990–2010) and nine years (2011–2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r2 value of 0.92. The seasonal ET0 estimated by modified Hargreaves–Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop when compared to irrigation scheduling by the FAO PM model, demonstrating the applicability of the developed model for irrigation in the semiarid region of the Punjab in India

Implementing Motor Unit Number Index (MUNIX) in a large clinical trial: Real world experience from 27 centres

OBJECTIVE: Motor Unit Number Index (MUNIX) is a quantitative neurophysiological method that reflects loss of motor neurons in Amyotrophic Lateral Sclerosis (ALS) in longitudinal studies. It has been utilized in one natural history ALS study and one drug trial (Biogen USA) after training and qualification of raters. METHODS: Prior to testing patients, evaluators had to submit test-retest data of 4 healthy volunteers. Twenty-seven centres with 36 raters measured MUNIX in 4 sets of 6 different muscles twice. Coefficient of variation of all measurements had to be <20% to pass the qualification process. MUNIX COV of the first attempt, number of repeated measurements and muscle specific COV were evaluated. RESULTS: COV varied considerably between raters. Mean COV of all raters at the first measurements was 12.9% ± 13.5 (median 8.7%). Need of repetitions ranged from 0 to 43 (mean 10.7 ± 9.1, median 8). Biceps and first dorsal interosseus muscles showed highest repetition rates. MUNIX variability correlated considerably with variability of compound muscle action potential. CONCLUSION: MUNIX revealed generally good reliability, but was rater dependent and ongoing support for raters was needed. SIGNIFICANCE: MUNIX can be implemented in large clinical trials as an outcome measure after training and a qualification process.status: publishe

Implementing Motor Unit Number Index (MUNIX) in a large clinical trial : Real world experience from 27 centres

Objective: Motor Unit Number Index (MUNIX) is a quantitative neurophysiological method that reflects loss of motor neurons in Amyotrophic Lateral Sclerosis (ALS) in longitudinal studies. It has been utilized in one natural history ALS study and one drug trial (Biogen USA) after training and qualification of raters. Methods: Prior to testing patients, evaluators had to submit test-retest data of 4 healthy volunteers. Twenty-seven centres with 36 raters measured MUNIX in 4 sets of 6 different muscles twice. Coefficient of variation of all measurements had to be <20% to pass the qualification process. MUNIX COV of the first attempt, number of repeated measurements and muscle specific COV were evaluated. Results: COV varied considerably between raters. Mean COV of all raters at the first measurements was 12.9% ± 13.5 (median 8.7%). Need of repetitions ranged from 0 to 43 (mean 10.7 ± 9.1, median 8). Biceps and first dorsal interosseus muscles showed highest repetition rates. MUNIX variability correlated considerably with variability of compound muscle action potential. Conclusion: MUNIX revealed generally good reliability, but was rater dependent and ongoing support for raters was needed. Significance: MUNIX can be implemented in large clinical trials as an outcome measure after training and a qualification process