scholarly journals Do Estimates of Water Productivity Enhance Understanding of Farm-Level Water Management?

Water ◽  
2014 ◽  
Vol 6 (4) ◽  
pp. 778-795 ◽  
Author(s):  
Dennis Wichelns
Keyword(s):  
Water Management ◽  
Water Productivity ◽  
Farm Level

Grain yield, water productivity and CH 4 emission of irrigated rice in response to water management in south China

2016 ◽  
Vol 163 ◽  
pp. 319-331 ◽  
Author(s):  
Kaiming Liang ◽  
Xuhua Zhong ◽  
Nongrong Huang ◽  
Rubenito M. Lampayan ◽  
Junfeng Pan ◽  
...  
Keyword(s):  
Water Management ◽  
Grain Yield ◽  
South China ◽  
Water Productivity ◽  
Irrigated Rice ◽  
Response To Water

scholarly journals Effect of Water Management Practices on Rice Yield, Water Productivity and Water Savings under Irrigated Rice Paddy Ecosystem

2017 ◽  
Vol 9 (2) ◽  
pp. 79-84 ◽  
Author(s):  
SC Barman ◽  
MA Ali ◽  
HJ Hiya ◽  
KR Sarker ◽  
MA Sattar
Keyword(s):  
Water Management ◽  
Water Level ◽  
Management Practices ◽  
Rice Yield ◽  
Water Productivity ◽  
Ground Level ◽  
Productivity Index ◽  
Water Savings ◽  
Maximum Water ◽  
Irrigation Treatments

A field experiment was carried out during the Boro season 2013 to find out the effects of water management practices on rice yield performance and water productivity index at Old Brahmaputra flood plain paddy land, Muktagacha, Mymensingh. The experiment was laid out in randomized complete block design (RCBD) with six (6) irrigation treatments. Two treatments, T1 and T3 were kept under continuous standing water levels (10 cm and 5 cm respectively) while in treatment T5 irrigation water was supplied for 1st 3 weeks then followed mid season drain out and re-flooded at flowering stage. Three alternate wetting and drying irrigation treatments, T2, T4 and T6 were selected in which irrigation water was applied when water level dropped 20cm, 10cm and 15cm below ground level, respectively. All the irrigation treatments significantly affected the rice yield and yield contributing parameters. The study revealed that the highest grain yield (5950 kg ha-1) was found in treatment T5 which was identical with AWDI treatment T4 (5820 kg ha-1) followed by AWDI treatment T6 (5460 kg ha-1). On the contrary, rice yield of 3350 kg ha-1, 4470 kg ha-1 and 4810 kg ha-1 were found in the treatment T1, T2 and T3, respectively. It was found that AWDI treatment T2 showed maximum water savings (15.1%) followed by T6 (11.3%), T4 (7.59%) and T5 (3.8%), however rice yield in the treatment T2 (4470 kg ha-1) was significantly lower compared to T6, T4 and T5 treatment. Therefore, it may be inferred that treatment T4 (AWDI; irrigation when water level fell 10 cm from ground level), T5 (Irrigation for 1st 3 weeks, then mid-season drain out and re-flooding at flowering) and T6 (AWDI; irrigation when water level fell 15cm from ground level) would be the feasible choice for the water savings, higher rice yield as well as maximum water productivity index (0.478, 0.472 and 0.467, respectively) for sustaining rice farming during the dry Boro season in Bangladesh.J. Environ. Sci. & Natural Resources, 9(2): 79-84 2016

A dynamic model for water management at the farm level integrating strategic, tactical and operational decisions

2018 ◽  
Vol 100 ◽  
pp. 123-135 ◽  
Author(s):  
Marion Robert ◽  
Alban Thomas ◽  
Muddu Sekhar ◽  
Hélène Raynal ◽  
Éric Casellas ◽  
...  
Keyword(s):  
Water Management ◽  
Dynamic Model ◽  
Operational Decisions ◽  
Farm Level

scholarly journals Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI

2021 ◽  
Vol 13 (14) ◽  
pp. 7967
Author(s):  
Usha Poudel ◽  
Haroon Stephen ◽  
Sajjad Ahmad
Keyword(s):  
Water Management ◽  
Sugar Beet ◽  
Crop Yield ◽  
Water Conservation ◽  
Irrigation System ◽  
Water Productivity ◽  
Google Earth ◽  
Geographical Information ◽  
Irrigation Performance ◽  
Crop Water

Southern California’s Imperial Valley (IV) faces serious water management concerns due to its semi-arid environment, water-intensive crops and limited water supply. Accurate and reliable irrigation system performance and water productivity information is required in order to assess and improve the current water management strategies. This study evaluates the spatially distributed irrigation equity, adequacy and crop water productivity (CWP) for two water-intensive crops, alfalfa and sugar beet, using remotely sensed data and a geographical information system for the 2018/2019 crop growing season. The actual crop evapotranspiration (ETa) was mapped in Google Earth Engine Evapotranspiration Flux, using the linear interpolation method in R version 4.0.2. The approx() function in the base R was used to produce daily ETa maps, and then totaled to compute the ETa for the whole season. The equity and adequacy were determined according to the ETa’s coefficient of variation (CV) and relative evapotranspiration (RET), respectively. The crop classification was performed using a machine learning approach (a random forest algorithm). The CWP was computed as a ratio of the crop yield to the crop water use, employing yield disaggregation to map the crop yield, using county-level production statistics data and normalized difference vegetation index (NDVI) images. The relative errors (RE) of the ETa compared to the reported literature values were 7–27% for alfalfa and 0–3% for sugar beet. The average ETa variation was low; however, the spatial variation within the fields showed that 35% had a variability greater than 10%. The RET was high, indicating adequate irrigation; 31.5% of the alfalfa and 12% of the sugar beet fields clustered in the Valley’s central corner were consuming more water than their potential visibly. The CWP showed wide variation, with CVs of 32.92% for alfalfa and 25.4% for sugar beet, signifying a substantial scope for CWP enhancement. The correlation between the CWP, ETa and yield showed that reducing the ETa to approximately 1500 mm for alfalfa and 1200 mm for sugar beet would help boost the CWP without decreasing the yield, which is nearly equivalent to 44.52M cu. m (36,000 acre-ft) of water. The study’s results could help water managers to identify poorly performing fields where water conservation and management could be focused.

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