Your search found 16 records
1 Karim, Z.; Nelson, L. J.; Idris, M.; Baxter, J. C.; Khan, C. M. A.; Oad, R. N.; Podmore, T. H.; Hossain, M. I.; Haider, M. I.; Karim, K. B.; Laitos, W. R. 1983. Diagnostic analysis of five deep tubewell irrigation systems in Joydebpur, Bangladesh. Fort Collins, CO, USA: University Services Centre. Colorado State University. xiii, 209p. (Water management synthesis report no.15)
Water management ; Evaluation ; Tube wells ; Rice ; Cropping systems ; Crop yield ; Yield forecasting ; Nitrogen ; Organizations ; Pumps ; Water rates ; Energy / Bangladesh
(Location: IWMI-HQ Call no: 631.7.6.3 G584 KAR Record No: H064)
2 Sison, J. F. 1985. Irrigation and rice productivity: the Philippine setting. Paper presented at the Workshop on Agricultural Policy, Los Banos, Philippines, 3-4 May 1985. 36p.
Rice ; Yield forecasting ; Policy ; Irrigation efficiency ; Irrigation systems ; Water management ; Investment ; Economic aspects ; Irrigated farming ; Governmental interrelations / Philippines
(Location: IWMI-HQ Call no: P 899 Record No: H0756)
This paper presents some of the research findings with regard to (1) the impact of irrigation on rice productivity, (2) reiterate the major problems affecting irrigation activities which, in turn, affect the country's rice productivity and (3) to derive implications for future irrigation policy.
3 1986. Paddy harvest record drop in Maha 1985/86. Economic Review, 12(5):20-21.
(Location: IWMI-HQ Call no: P 1914 Record No: H01435)
4 Sinha, S. K.; Aggarwal, P. K.; Khanna-Chopra, R. 1985. Irrigation in India: A physiological and phenological approach to water management in grain crops. In D. Hillel, Advances in irrigation. Vol. 3 (pp. 130-206). Orlando, FL, USA: Academic Press.
(Location: IWMI-HQ Call no: 631.7 G000 HIL Record No: H01804)
5 Bheemaiah, G.; Chary, A. V. 1985. Effect of different stream sizes and length of borders on irrigation parameters, on yield of sesamum crop. Journal of Research APAU, 1(1):1-6.
(Location: IWMI-HQ Call no: P 1203 Record No: H02345)
6 Khan, L. R.; Mawdsley, J. A. 1986. Groundwater yield assessment of a stream aquifer system using a lumped model. In Regional Workshop on Groundwater Modelling, Roorkee, 12-17 December 1986. Roorkee, India: Water Resources Development Training Center, University of Roorkee. pp.121-138.
(Location: IWMI-HQ Call no: 631.7.6.3 G635 REG Record No: H02941)
7 Arora, V. K.; Prihar, S. S.; Gajri, P. R. 1987. Synthesis of a simplified water use efficiency model for predicting wheat yields. Water Resources Research, 23(5):903-910.
Irrigation ; Water use ; Simulation models ; Evapotranspiration ; Water balance ; Yield forecasting ; Wheat
(Location: IWMI-HQ Call no: PER Record No: H03190)
8 Martin, E. C.; Ritchie, J. T.; Loudon, T. L. 1985. Use of the CERES-Maize model to evaluate irrigation strategies for humid regions. In American Society of Agricultural Engineers, Advances in evapotranspiration: Proceedings of the National Conference on Advances in Evapotranspiration, Chicago, Illinois, 16-17 December 1985. St. Joseph, MI, USA: ASAE. pp.342-356. (ASAE publication 14-85)
Simulation models ; Irrigation scheduling ; Evaluation ; Decision making ; Water balance ; Irrigation effects ; Yield forecasting
(Location: IWMI-HQ Call no: 631.7.5 G000 AME Record No: H03350)
9 Nanayakkara, A. G. W. 1987. Progress in paddy cultivation and production in Sri Lanka and forecasts for the future. Colombo, Sri Lanka: Ministry of Plan Implementation. Department of Census and Statistics. ii, 22p. (New monograph series 1)
(Location: IWMI-HQ Call no: 633.1 G744 NAN Record No: H03401)
10 1974. A study of the impact of green revolution on the regional development of agriculture in Uttar Pradesh. Indian Journal of Agricultural Economics, 29(4):44-54.
Regional planning ; Yield forecasting ; Technology ; Agricultural production ; Green revolution / India / Uttar Pradesh
(Location: IWMI-HQ Call no: P 1385 Record No: H04718)
11 Yike, X.; Zhanhua, C. 1991. The yield reduction pattern of rice due to drought and its application. In ICID, The Special Technical Session: Proceedings, Beijing, China, April 1991. Vol.1-A: Irrigation planning. New Delhi, India: ICID. pp.181-192.
(Location: IWMI-HQ Call no: ICID 631.7 G000 ICI Record No: H014900)
12 Khamala, E. 2017. Review of the available remote sensing tools, products, methodologies and data to improve crop production forecasts. Rome, Italy: FAO. 94p.
Remote sensing ; Crop production ; Yield forecasting ; Crop modelling ; Early warning systems ; Drought ; Rain ; Global observing systems ; GIS ; Satellite observation ; Satellite imagery ; Microwave radiation ; Maps ; Statistical data ; Agricultural statistics ; Vegetation index ; Indicators ; National organizations ; Agencies / Africa South of Sahara / Kenya / Senegal / Zimbabwe
(Location: IWMI HQ Call no: e-copy only Record No: H048227)
(1.80 MB) (1.80 MB)
13 Traore, S.; Zhang, L.; Guven, A.; Fipps, G. 2020. Rice yield response forecasting tool (YIELDCAST) for supporting climate change adaptation decision in Sahel. Agricultural Water Management, 239:106242. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106242]
Climate change adaptation ; Upland rice ; Crop yield ; Yield forecasting ; Decision support systems ; Models ; Gene expression ; Rainfed farming ; Temperature ; Carbon dioxide ; Emission / Sahel / Burkina Faso / Bobo Dioulasso
(Location: IWMI HQ Call no: e-copy only Record No: H049705)
(2.71 MB)
Rice yield responses forecast (YIELDCAST) is a very useful decision support tool in climate adaptation in Sahel, where crops are purely rainfed climate-stressors sensitive. This study aims to construct upland rice yield responses forecasting algebraic formulation code referred as YIELDCAST by using gene-expression programming (GEP) based on observed rainfall and temperatures data (1979–2011), and forcing with global climate model (GCM) downscaled outputs under CO2 emission scenarios SR-A1B, A2 and B1 (2012–2100) over Bobo-Dioulasso, a Sahelian region. Statistically, GEP is a capable tool to downscale climate variables in the region (R = 0.746-0.949), and construct reliable rice YIELDCAST tool (R = 0.930; MSE = 0.037 ton/ha; MAE = 0.155 ton/ha, RSE = 0.137 ton/ha). Yields forecasted (2012–2100) showed a noticeable statistically significant difference between scenarios; however, fluctuating with no substantial increase (average below 1.60 ton/ha); suggesting that the increase observed in temperatures and decrease in rains will either reduced or hindered yield to largely increase in Sahel. With no such YIELDCAST tool to support adaptation decision, Sahel will still be under the trap of the broad array of adaptation strategy, which is a trial and error, less specific and costly. The model can help anticipate adaptation decision support on-farm water management, shift to suitable planting periods, and use of improved drought resistant and short duration varieties adapted to a local weather pattern.
14 Alvar-Beltran, J.; Soldan, R.; Ly, P.; Seng, V.; Srun, K.; Manzanas, R.; Franceschini, G.; Heureux, A. 2022. Climate change impacts on irrigated crops in Cambodia. Agricultural and Forest Meteorology, 324:109105. [doi: https://doi.org/10.1016/j.agrformet.2022.109105]
Irrigated farming ; Climate change ; Irrigation methods ; Crop production ; Vegetables ; Tomatoes ; Pak choi ; Asparagus beans ; Yield forecasting ; Water productivity ; Drought stress ; Precipitation ; Models / Cambodia / Siem Reap / Tonle Sap Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051398)
https://www.sciencedirect.com/science/article/pii/S0168192322002921/pdfft?md5=9688ebfcf2d983d35d219fa2bbfec7c7&pid=1-s2.0-S0168192322002921-main.pdf
https://vlibrary.iwmi.org/pdf/H051398.pdf
https://vlibrary.iwmi.org/pdf/H051398.pdf
(11.40 MB) (11.4 MB)
Increasing heat-stress conditions, rising evaporative demand and shifting rainfall patterns may have multifaceted impacts on Cambodia's agricultural systems, including vegetable production. Concurrently, domestic vegetable supply is highly seasonal and inadequate to meet the domestic food demand, which consequently poses risks to food security locally, particularly in rural areas. This study assesses the impact of climate change on the yields and crop water productivity (CWP) of tomato, pak choi and yard-long bean cultivated year-round under different irrigated conditions (drip, furrow and net irrigation) in Siem Reap, Cambodia. The findings of this study show a similar annual precipitation decline (-23%) when comparing the 2017–2040 and 2070–2099 periods for both Representative Concentration Pathways (RCPs 4.5 and 8.5), though with significant seasonal differences between the two climate scenarios. Increasing water and heat-stress conditions are expected to have adverse impacts on tomato plants compared to pak choi and yard-long bean, which have a much higher heat tolerance. Differing yield trends are expected depending on the transplanting/sowing date, irrigation method and RCP. In tomato, for example, a -55% yield loss is projected by the end-century (2070–2099) when transplanting in January, whereas a + 37% yield increase is expected between November and December over the same period. In addition, pak choi yield enhancements of up to +30% are projected if sowing in May under RCP 8.5 for both drip and net irrigation conditions. Similarly, higher yard-long bean yields are simulated under RCP 8.5 (+29%) compared to RCP 4.5 (+11%) for the average of all sowing dates (January to December) and irrigation methods (drip, furrow and net irrigation). In sum, the findings of this work are relevant for evidence-based decision-making and the development of projects, policies and programmes increasingly informed by simulation results from bundling climate-crop approaches to transform agriculture in response to climate change.
15 Tomasella, J.; Martins, M. A.; Shrestha, Nirman. 2023. An open-source tool for improving on-farm yield forecasting systems. Frontiers in Sustainable Food Systems, 7:1084728. [doi: https://doi.org/10.3389/fsufs.2023.1084728]
Yield forecasting ; Crop forecasting ; Soil fertility ; Irrigation management ; Yield gap ; Crop modelling ; Optimization ; On-farm research ; Wheat ; Maize ; Soil water content ; Water productivity ; Biomass ; Canopy ; Climate change ; Assessment ; Computer software / Tunisia / Nepal / Brazil / Tunis / Chitwan / Araripina
(Location: IWMI HQ Call no: e-copy only Record No: H052083)
https://www.frontiersin.org/articles/10.3389/fsufs.2023.1084728/pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
(6.59 MB) (6.59 MB)
Introduction: The increased frequency of extreme climate events, many of them of rapid onset, observed in many world regions, demands the development of a crop forecasting system for hazard preparedness based on both intraseasonal and extended climate prediction. This paper presents a Fortran version of the Crop Productivity Model AquaCrop that assesses climate and soil fertility effects on yield gap, which is crucial in crop forecasting systems
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
16 Sibanda, M.; Buthelezi, S.; Mutanga, O.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Gokool, S.; Naiken, V.; Magidi, J.; Mabhaudhi, Tafadzwanashe. 2023. Exploring the prospects of UAV-remotely sensed data in estimating productivity of maize crops in typical smallholder farms of Southern Africa. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023:1143-1150. (ISPRS Geospatial Week 2023, Cairo, Egypt, 2-7 September 2023) [doi: https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1143-2023]
Agricultural productivity ; Small farms ; Smallholders ; Maize ; Yield forecasting ; Models ; Remote sensing ; Unmanned aerial vehicles ; Vegetation index / Southern Africa / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H052490)
https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1143/2023/isprs-annals-X-1-W1-2023-1143-2023.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
(1.59 MB) (1.59 MB)
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors.
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