Your search found 11 records
1 Tripathi, V. K.; Rajput, T. B. S.; Patel, N.; Kumar, P. 2016. Effects on growth and yield of eggplant (Solanum melongema L.) under placement of drip laterals and using municipal wastewater. Irrigation and Drainage, 65(4):480-490. [doi: https://doi.org/10.1002/ird.1971]
Wastewater irrigation ; Wastewater treatment ; Vegetable growing ; Solanum melongena ; Crop yield ; Periurban areas ; Irrigation systems ; Drip irrigation ; Groundwater irrigation ; Subsurface irrigation ; Irrigation equipment ; Performance evaluation ; Chemicophysical properties ; Soils ; Leaf Area Index ; Root length ; Dry matter content ; Models / India / New Delhi
(Location: IWMI HQ Call no: e-copy only Record No: H047798)
(0.49 MB)
This study was conducted to utilize municipal wastewater through surface and subsurface (15 and 30 cm) drip systems. Wastewater was treated though media type, disk type and combined media and disk type filters. The field experiment was conducted for two years with wastewater and groundwater. Root length density (RLD), leaf area index (LAI), and fruit yield with dry matter was recorded. LAI was lower under subsurface drip during the initial 55 days after transplantation, but in later stages it was significantly higher in comparison to surface drip. Highest RLD of 3.6 cm cm–3 was recorded under subsurface drip at 30 cm depth. RLD and LAI were related with a correlation coefficient value of 0.69. Highest dry matter content (8.75%) was recorded under surface drip but highest fruit yield was recorded under subsurface placement of drip at 15 cm depth. Subsurface drip at 15 and 30 cm depths resulted in 12.4 and 8.5% higher yields respectively, in comparison with surface drip. Utilization of wastewater through a drip irrigation system has given 6.2% increase in eggplant fruit yield in comparison to groundwater irrigation, with savings of 47, 18 and 40% of N, P2O5 and K2O nutrients respectively. The findings of the present study elucidate the potentials and constraints of wastewater utilization through a subsurface drip system.
2 Sreelash, K.; Buis, S.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Guerif, M. 2017. Estimation of available water capacity components of two-layered soils using crop model inversion: effect of crop type and water regime. Journal of Hydrology, 546:166-178. [doi: https://doi.org/10.1016/j.jhydrol.2016.12.049]
Water holding capacity ; Water availability ; Estimation ; Soil water content ; Soil hydraulic properties ; Layered soils ; Soil moisture ; Field capacity ; Wilting point ; Water stress ; Crop management ; Models ; Sensitivity analysis ; Leaf Area Index ; Maize ; Sorghum ; Sunflowers ; Turmeric ; Remote sensing ; Catchment areas / South India / Berambadi Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H048041)
(1.43 MB)
Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.
3 Marandi, M.; Parida, B. R.; Ghosh, Surajit. 2022. Retrieving vegetation biophysical parameters and GPP [Gross Primary Production] using satellite-driven LUE [Light Use Efficiency] model in a national park. Environment, Development and Sustainability, 24(7):9118-9138. [doi: https://doi.org/10.1007/s10668-021-01815-0]
Normalized difference vegetation index ; Photosynthetically active radiation ; Air temperature ; Moisture ; Leaf Area Index ; Land use ; Land cover ; National parks ; Satellite observation ; Moderate resolution imaging spectroradiometer ; Models / India / Assam / Dibru Saikhowa National Park
(Location: IWMI HQ Call no: e-copy only Record No: H050796)
(3.70 MB)
The terrestrial biosphere plays an active role in governing the climate system by regulating carbon exchange between the land and the atmosphere. Analysis of vegetation biophysical parameters and gross primary production (GPP) makes it convenient to monitor vegetation's health. A light use efficiency (LUE) model was employed to estimate daily GPP from satellite-driven data and environmental factors. The LUE model is driven by four major variables, namely normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and moisture for which both satellite-based and ERA5-Land data were applied. In this study, the vegetation health of Dibru Saikhowa National Park (DSNP) in Assam has been analyzed through vegetation biophysical and biochemical parameters (i.e., NDVI, EVI, LAI, and chlorophyll content) using Sentinel-2 data. Leaf area index (LAI) varied between 1 and 5.2, with healthy forests depicted LAI more than 2.5. Daily GPP was estimated for January (winter) and August (monsoon) 2019 for tropical evergreen and deciduous forest types. A comparative analysis of GPP for two seasons has been performed. In January, GPP was found to be 3.6 gC m-2 day-1, while in August, GPP was 5 gC m-2 day-1. The outcome of this study may be constructive to forest planners to manage the National Park so that net carbon sink may be attained in DSNP.
4 Masenyama, A.; Mutanga, O.; Dube, T.; Bangira, T.; Sibanda, M.; Mabhaudhi, T. 2022. A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services. GIScience and Remote Sensing, 59(1):1000-1025. [doi: https://doi.org/10.1080/15481603.2022.2088652]
Grasslands ; Ecosystem services ; Remote sensing ; Technology ; Earth observation satellites ; Hydrological modelling ; Systematic reviews ; Biomass ; Leaf area index ; Canopy ; Vegetation index ; Sensors ; Water management ; Monitoring ; Machine learning ; Forecasting
(Location: IWMI HQ Call no: e-copy only Record No: H051246)
https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2088652
https://vlibrary.iwmi.org/pdf/H051246.pdf
https://vlibrary.iwmi.org/pdf/H051246.pdf
(3.82 MB) (3.82 MB)
The last decade has seen considerable progress in scientific research on vegetation ecosystem services. While much research has focused on forests and wetlands, grasslands also provide a variety of different provisioning, supporting, cultural, and regulating services. With recent advances in remote sensing technology, there is a possibility that Earth observation data could contribute extensively to research on grassland ecosystem services. This study conducted a systematic review on progress, emerging gaps, and opportunities on the application of remote sensing technologies in quantifying all grassland ecosystem services including those that are related to water. The contribution of biomass, Leaf Area Index (LAI), and Canopy Storage Capacity (CSC) as water-related ecosystem services derived from grasslands was explored. Two hundred and twenty-two peer-reviewed articles from Web of Science, Scopus, and Institute of Electrical and Electronics Engineers were analyzed. About 39% of the studies were conducted in Asia with most of the contributions coming from China while a few studies were from the global south regions such as Southern Africa. Overall, forage provision, climate regulation, and primary production were the most researched grassland ecosystem services in the context of Earth observation data applications. About 39 Earth observation sensors were used in the literature to map grassland ecosystem services and MODIS had the highest utilization frequency. The most widely used vegetation indices for mapping general grassland ecosystem services in literature included the red and near-infrared sections of the electromagnetic spectrum. Remote sensing algorithms used within the retrieved literature include process-based models, machine learning algorithms, and multivariate techniques. For water-related grassland ecosystem services, biomass, CSC, and LAI were the most prominent proxies characterized by remotely sensed data for understanding evapotranspiration, infiltration, run-off, soil water availability, groundwater restoration and surface water balance. An understanding of such hydrological processes is crucial in providing insights on water redistribution and balance within grassland ecosystems which is important for water management.
5 Wu, S.; Deng, L.; Guo, L.; Wu, Y. 2022. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery. Plant Methods, 18:68. [doi: https://doi.org/10.1186/s13007-022-00899-7]
Leaf area index ; Forecasting ; Unmanned aerial vehicles ; Thermal infrared imagery ; Data fusion ; Machine learning ; Estimation ; Wheat ; Vegetation index ; Remote sensing ; Satellites ; Biomass ; Models / China / Henan
(Location: IWMI HQ Call no: e-copy only Record No: H051401)
https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00899-7.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
(7.53 MB) (7.53 MB)
Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
6 Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6):1597. (Special issue: Retrieving Leaf Area Index Using Remote Sensing) [doi: https://doi.org/10.3390/rs15061597]
Maize ; Leaf area index ; Vegetation index ; Remote sensing ; Unmanned aerial vehicles ; Multispectral imagery ; Small-scale farming ; Smallholders ; Growth stages ; Monitoring ; Forecasting ; Models ; Machine learning ; Agricultural productivity ; Crop yield / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H051818)
https://www.mdpi.com/2072-4292/15/6/1597/pdf?version=1678869485
https://vlibrary.iwmi.org/pdf/H051818.pdf
https://vlibrary.iwmi.org/pdf/H051818.pdf
(3.96 MB) (3.96 MB)
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.
7 Masenyama, A.; Mutanga, O.; Dube, T.; Sibanda, M.; Odebiri, O.; Mabhaudhi, T. 2023. Inter-seasonal estimation of grass water content indicators using multisource remotely sensed data metrics and the cloud-computing Google Earth Engine platform. Applied Sciences, 13(5):3117. (Special issue: Remote Sensing Applications in Agricultural, Earth and Environmental Sciences) [doi: https://doi.org/10.3390/app13053117]
Grasslands ; Plant water relations ; Estimation ; Remote sensing ; Datasets ; Leaf area index ; Vegetation index ; Climatic factors ; Indicators ; Satellite observation ; Forecasting ; Spatial distribution ; Models / South Africa / KwaZulu-Natal / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051820)
https://www.mdpi.com/2076-3417/13/5/3117/pdf?version=1677581546
https://vlibrary.iwmi.org/pdf/H051820.pdf
https://vlibrary.iwmi.org/pdf/H051820.pdf
(4.12 MB) (4.12 MB)
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m-2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m-2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R 2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m-2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m-2 and R 2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m-2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m-2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
8 Bangira, T.; Mutanga, O.; Sibanda, M.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing grassland productivity attributes: a systematic review. Remote Sensing, 15(8):2043. [doi: https://doi.org/10.3390/rs15082043]
Grasslands ; Productivity ; Prediction ; Remote sensing ; Estimation ; Monitoring ; Techniques ; Ecosystem services ; Leaf area index ; Above ground biomass ; Canopy ; Chlorophylls ; Nitrogen content ; Vegetation index
(Location: IWMI HQ Call no: e-copy only Record No: H051841)
https://www.mdpi.com/2072-4292/15/8/2043/pdf?version=1681347101
https://vlibrary.iwmi.org/pdf/H051841.pdf
https://vlibrary.iwmi.org/pdf/H051841.pdf
(3.26 MB) (3.26 MB)
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.
9 Patra, K.; Parihar, C. M.; Nayak, H. S.; Rana, B.; Sena, Dipaka R.; Anand, A.; Reddy, K. S.; Chowdhury, M.; Pandey, R.; Kumar, A.; Singh, L. K.; Ghatala, M. K.; Sidhu, H. S.; Jat, M. L. 2023. Appraisal of complementarity of subsurface drip fertigation and conservation agriculture for physiological performance and water economy of maize. Agricultural Water Management, 283:108308. [doi: https://doi.org/10.1016/j.agwat.2023.108308]
Conservation agriculture ; Subsurface irrigation ; Drip fertigation ; Drip irrigation ; Nitrogen-use efficiency ; Water productivity ; Maize ; Photosynthesis ; Irrigation management ; Irrigation water ; Irrigation methods ; Water-use efficiency ; Tillage ; Residues ; Leaf area index ; Crop yield ; Economic analysis ; Technology / India / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H051898)
https://www.sciencedirect.com/science/article/pii/S0378377423001737/pdfft?md5=f53db56ada3c45b634c4587196f5b402&pid=1-s2.0-S0378377423001737-main.pdf
https://vlibrary.iwmi.org/pdf/H051898.pdf
https://vlibrary.iwmi.org/pdf/H051898.pdf
(3.00 MB) (3.00 MB)
The Indo-Gangetic Plains (IGP) in north-west (NW) India are facing a severe decline in ground water due to prevalent rice-based cropping systems. To combat this issue, conservation agriculture (CA) with an alternative crop/s, such as maize, is being promoted. Recently, surface drip fertigation has also been evaluated as a viable option to address low-nutrient use efficiency and water scarcity problems for cereals. While the individual benefits of CA and sub-surface drip (SSD) irrigation on water economy are well-established, information regarding their combined effect in cereal-based systems is lacking. Therefore, we conducted a two-year field experiment in maize, under an ongoing CA-based maize-wheat system, to evaluate the complementarity of CA with SSD irrigation through two technological interventions–– CA+ (residue retained CA + SSD), PCA+ (partial CA without residue + SSD) – at different N rates (0, 120 and 150 kg N ha-1) in comparison to traditional furrow irrigated (FI) CA and conventional tillage (CT) at 120 kg N ha-1. Our results showed that CA+ had the highest grain yield (8.2 t ha-1), followed by PCA+ (8.1 t ha-1). The grain yield under CA+ at 150 kg N ha-1 was 27% and 30% higher than CA and CT, respectively. Even at the same N level (120 kg N ha-1), CA+ outperformed CA and CT by 16% and 18%, respectively. The physiological performance of maize also revealed that CA+ based plots with 120 kg N ha-1 had 12% and 3% higher photosynthesis rate at knee-high and silking, respectively compared to FI-CA and CT. Overall, compared to the FI-CA and CT, SSD-based CA+ and PCA+ saved 54% irrigation water and increased water productivity (WP) by more than twice. Similarly, a greater number of split N application through fertigation in PCA+ and CA+ increased agronomic nitrogen use efficiency (NUE) and recover efficiency by 8–19% and 14–25%, respectively. Net returns from PCA+ and CA+ at 150 kg N ha-1 were significantly higher by US$ 491 and 456, respectively than the FI-CA and CT treatments. Therefore, CA coupled with SSD provided tangible benefits in terms of yield, irrigation water saving, WP, NUE and profitability. Efforts should be directed towards increasing farmers’ awareness of the benefits of such promising technology for the cultivating food grains and commercial crops such as maize. Concurrently, government support and strict policies are required to enhance the system adaptability.
10 Mbangi, A.; Nongqwenga, N.; Mabhaudhi, T. 2023. Calibration accuracy of requirement factor and sorption studies for fertilizer recommendation. Agrosystems, Geosciences and Environment, 6(3):e20401. [doi: https://doi.org/10.1002/agg2.20401]
Fertilizer application ; Sorption isotherms ; Phosphorus ; Potassium ; Equilibration ; Plant growth ; Parameters ; Biomass ; Leaf area index ; Crop yield ; Soil solution ; Physicochemical properties ; Cowpeas ; Mustard ; Maize / South Africa
(Location: IWMI HQ Call no: e-copy only Record No: H052103)
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agg2.20401
https://vlibrary.iwmi.org/pdf/H052103.pdf
https://vlibrary.iwmi.org/pdf/H052103.pdf
(0.20 MB) (209 KB)
The inconsistent and incoherent approaches by fertilizer recommendations to index crop response has prompted the search for alternative approaches. Some of the problems stem from the overlooking of fundamental soil properties that govern the soil solution, which is where plant roots absorb nutrients for growth. A comparison was made between two contrasting equilibration techniques to evaluate their precision in estimating crop response. Sorption isotherms for phosphorus (P) and potassium (K) were compared to requirement factors. Phosphorus sorption isotherms were determined following the batch equilibration technique. Potassium was developed following equilibration with graded K levels. The requirement factors of both P and K were determined following a 6-week incubation with four different levels of fertilization. Cowpea (Vigna unguiculata), mustard (Brassica juncea), and maize (Zea mays) were used as test crops. The growth parameters measured included biomass (g), height (cm), and leaf area index. At harvest, yield (g pot-1) and uptake (mg pot-1) were also recorded. Linear correlation studies were carried out to evaluate the association between treatments and the growth parameters of the tested crops. Results showed no significant difference (p < 0.05) in maize growth parameters between the equilibration methods, despite the sorption isotherms estimating higher levels of P and K. The sorption isotherms for P and K were 1.7 and 9.8 times higher than their respective requirement factors. The crop response, although relatively similar in both methods, was weakly correlated with the sorption-estimated nutrient levels, indicating an overestimation of nutrients. Therefore, the requirement factors were deemed to be a more precise equilibration technique for estimating nutrient levels.
11 Sibanda, M.; Ndlovu, H. S.; Brewer, K.; Buthelezi, S.; Matongera, T. N.; Mutanga, O.; Odidndi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system. Smart Agricultural Technology, 6:100325. [doi: https://doi.org/10.1016/j.atech.2023.100325]
Crop damage ; Hail damage ; Maize ; Remote sensing ; Smallholders ; Farmers ; Farmland ; Small-scale farming ; Unmanned aerial vehicles ; Plant health ; Leaf area index ; Vegetation index ; Agricultural productivity ; Climate change / South Africa / KwaZulu Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052320)
https://www.sciencedirect.com/science/article/pii/S2772375523001545/pdfft?md5=79fa6611a7221a58090e695d915834b8&pid=1-s2.0-S2772375523001545-main.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
(17.40 MB) (17.4 MB)
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm- 2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m- 2 and 76.2 µmol m- 2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2 /m2 and 0.19 m2 /m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.
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