Your search found 6 records
1 Benli, B.; Kodal, S. 2003. A non-linear model for farm optimization with adequate and limited water supplies application to the South–East Anatolian Project (GAP) Region. Agricultural Water Management, 62(3):187-203.
(Location: IWMI-HQ Call no: PER Record No: H033249)
2 Benli, B.; Kodal, S.; Ilbeyi, A.; Ustun, H. 2006. Determination of evapotranspiration and basal crop coefficient of alfalfa with a weighing lysimeter. Agricultural Water Management, 81(3):358-370.
(Location: IWMI-HQ Call no: PER Record No: H038542)
(Location: IWMI-HQ Call no: PER Record No: H038697)
4 Singh, P.; Aggarwal, P. K.; Bhatia, V. S.; Murty, M. V. R.; Pala, M.; Oweis, T.; Benli, B.; Rao, K. P. C.; Wani, S. P. 2009. Yield gap analysis: modelling of achievable yields at farm level. In Wani, S. P.; Rockstrom, J.; Oweis, T. (Eds.). Rainfed agriculture: unlocking the potential. Wallingford, UK: CABI; Patancheru, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Colombo, Sri Lanka: International Water Management Institute (IWMI) pp.81-123. (Comprehensive Assessment of Water Management in Agriculture Series 7)
(Location: IWMI HQ Call no: IWMI 631.586 G000 WAN Record No: H041995)
5 Pala, M.; Oweis, T.; Benli, B.; De Pauw, E.; El Mourid, M.; Karrou, M.; Jamal, M.; Zencirci, N. 2011. Assessment of wheat yield gap in the Mediterranean: case studies from Morocco, Syria and Turkey. Aleppo, Syria: International Center for Agricultural Research in the Dry Areas (ICARDA). 36p.
(Location: IWMI HQ Call no: 630 G000 PAL Record No: H044940)
(2.05 MB) (2.3MB)
(Location: IWMI HQ Call no: e-copy only Record No: H047967)
(2.62 MB)
When estimating canal water supplies for large-scale irrigation schemes and especially in arid regions worldwide, the impact of all factors affecting the gross irrigation requirements (GIR) are not properly accounted for, which results in inefficient use of precious freshwater resources. This research shows that the concept of irrigation response units (IRU)—areas having unique combinations of factors effecting the GIR—allows for more precise estimates of GIR. An overlay analysis of soil texture and salinity, depth and salinity of groundwater, cropping patterns and irrigation methods was performed in a GIS environment, which yielded a total of 17 IRUs combinations of the Oktepa Zilol Chashmasi water consumers’ association in multi-country Fergana Valley, Central Asia. Groundwater contribution, leaching requirements, losses in the irrigation system through field application and conveyance and effective rainfall were included in GIR estimates. The GIR varied significantly among IRUs [average of 851 mm (±143 mm)] with a maximum (1051 mm) in IRU-12 and a minimum (629 mm) in IRUs-15, 16. Owing to varying groundwater levels in each IRU, the groundwater contribution played a key role in the estimation of the GIR. The maximum groundwater contribution occurred in IRUs dominated by cotton–fallow rotations as evidenced by an average value of 159 mm but a maximum of 254 mm and a minimum of 97 mm. Percolation losses depended on irrigation methods for different crops in their respective IRUs. The novel approach can guide water managers in this and similar regions to increase the accuracy of irrigation demands based on all the factor effecting the GIR.
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