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(Location: IWMI HQ Call no: e-copy only Record No: H050012)
(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.
(Location: IWMI HQ Call no: e-copy only Record No: H050054)
(6.03 MB) (6.03 MB)
This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.
(Location: IWMI HQ Call no: e-copy only Record No: H051019)
(5.25 MB) (5.25 MB)
UAV-based multispectral vegetation indices are often used to assess crop performance and water consumptive use. However, their ability to assess the interaction between water, especially deficit irrigation, and nitrogen application rates in irrigated agriculture has been less explored. Understanding the effect of water-nitrogen interactions on vegetation indices could further support optimal water and N management. Therefore, this study used a split plot design with water being the main factor and N being the sub-factor. African eggplants were drip irrigated at 100% (I100), 80% (I80) or 60% (I60) of the crop water requirements and received 100% (F100), 75% (F75), 50% (F50) or 0% (F0) of the crop N requirements. Results showed that the transformed difference vegetation index (TDVI) was best in distinguishing differences in leaf moisture content (LMC) during the vegetative stage irrespective of the N treatment. The green normalized difference vegetation index (GNDVI) worked well to distinguish leaf N during vegetative and full vegetative stages. However, the detection of the interactive effect of water and N on crop performance required a combination of GNDVI, NDVI and OSAVI across both stages as each of these 3 VI showed an ability to detect some but not all treatments. The fact that a certain amount of irrigation water can optimize the efficiency of N uptake by the plant is an important criterion to consider in developing crop specific VI based decision trees for crop performance assessments and yield prediction.
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