Your search found 3 records
1 Ahmad, M. J.; Cho, G.-H.; Kim, S.-H.; Lee, S.; Adelodun, B.; Choi, K.-S. 2020. Influence mechanism of climate change over crop growth and water demands for wheat-rice system of Punjab, Pakistan. Journal of Water and Climate Change, 18p. (Online first) [doi: https://doi.org/10.2166/wcc.2020.009]
Climate change ; Forecasting ; Crop growth stage ; Water demand ; Wheat ; Rice ; Evapotranspiration ; Irrigation water ; Water requirements ; Crop water use ; Rain ; Models / Pakistan / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H049883)
(1.19 MB)
Conceptualizing the climate change perspective of crop growth and evapotranspiration (ETc) rates and subsequent irrigation water requirements (IWR) is necessary for sustaining the agriculture sector and tackling food security issues in Pakistan. This article projects the future growth periods and water demands for the wheat-rice system of Punjab. Intense and hotter transitions in the future thermal regimes and erratic monsoon rainfall increments were envisaged. The crop growth rates were accelerated by the probable temperature rise resulting in shortened growth periods. The temperature rise increased the reference evapotranspiration rates; however, the future ETc declined due to reduced growth period and net radiation. Highly unpredictable, but mostly increasing, cumulative seasonal and annual rainfalls were indicative of more effective rainfalls during the future crop seasons. Reduced ETc and increments in seasonal effective rainfalls gave rise to the declining IWR for both crops. The study findings seemingly undermined the harmful climate change influences on the water requirements of the wheat-rice system of Punjab but alarmingly shortening of growth periods indicates a higher crop failure tendency under the projected future thermal regime.
2 Mwinuka, P. R.; Mbilinyi, B. P.; Mbungu, W. B.; Mourice, S. K.; Mahoo, H. F.; Schmitter, Petra. 2021. Optimizing water and nitrogen application for neglected horticultural species in tropical sub-humid climate areas: a case of African eggplant (Solanum aethiopicum L.). Scientia Horticulturae, 276:109756. [doi: https://doi.org/10.1016/j.scienta.2020.109756]
Water use efficiency ; Nitrogen fertilizers ; Fertilizer application ; Fruit vegetables ; Eggplants ; Horticulture ; Solanum aethiopicum ; Crop water use ; Water requirements ; Drip irrigation ; Crop growth stage ; Crop yield ; Performance indexes ; Subhumid climate ; Soil chemicophysical properties / Africa / United Republic of Tanzania / Rudewa
(Location: IWMI HQ Call no: e-copy only Record No: H050012)
https://www.sciencedirect.com/science/article/pii/S0304423820305847/pdfft?md5=f79f2516a52f7afe55f8cb9d3fb8a4d2&pid=1-s2.0-S0304423820305847-main.pdf
https://vlibrary.iwmi.org/pdf/H050012.pdf
https://vlibrary.iwmi.org/pdf/H050012.pdf
(2.42 MB) (2.42 MB)
African eggplant, a traditional and important nutrient-dense crop to Tanzania’s nutrition and food security. However, yields remain low as a result of sub-optimal irrigation and fertilizer practices. To reduce the yield gap, a randomized split-plot design set up with irrigation as a main and nitrogen (N) treatments as a sub-factor. The irrigation regimes were 100 % (I100), 80 % (I80) and 60 % (I60) of crop water requirements whilst nitrogen levels were 250 kg N/ha (F100), 187 kg N/ha (F75), 125 kg N/ha (F50) and 0 kgN/ha (F0). The study evaluated the effect of irrigation water and N on crop growth variables and yield, fruit quality, WUE and NUE. The study showed the importance of combining different irrigation performance indicators which responds to different levels of water and nitrogen to evaluate and assess suitable irrigation and fertilizer strategies for African eggplant. The crop growth variables (plant height and LAI) had a good correlation with fruit yield (R2 = 0.6 and 0.8). The fruit quality was best performed by 100 % water in combination with 75 % N treatment. The best WUE and NUE was attained at 80 % and 100 % levels of water in combination with 75 % N. However, minimizing trade-offs between the various indicators, the optimal application for African eggplant would likely be around 80 % of the total irrigation requirement and 75 % of the N requirement in sandy clay loam soils under tropical sub-humid conditions.
3 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Odindi, J.; Mutanga, O.; Naiken, V.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2022. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform. Drones, 6(7):169. [doi: https://doi.org/10.3390/drones6070169]
Crop growth stage ; Maize ; Temperature measurement ; Stomatal conductance ; Estimation ; Water stress ; Thermal infrared imagery ; Unmanned aerial vehicles ; Machine learning ; Forecasting ; Models ; Precision agriculture ; Smallholders ; Small-scale farming ; Crop water use ; Indicators / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H051298)
https://www.mdpi.com/2504-446X/6/7/169/pdf?version=1657704795
https://vlibrary.iwmi.org/pdf/H051298.pdf
https://vlibrary.iwmi.org/pdf/H051298.pdf
(7.44 MB) (7.44 MB)
Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.
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