Your search found 19 records
1 Fensholt, R.; Sandholt, I.; Rasmussen, M. S.; Stisen, S.; Diouf, A. 2006. Evaluation of satellite based primary production modelling in the semi-arid Sahel. Remote Sensing of Environment, 105:173-188.
Remote sensing ; Satellite surveys ; Models ; Water stress ; Canopy ; Regression analysis / Africa / Sahel
(Location: IWMI-HQ Call no: P 7683 Record No: H039478)
2 Thenkabail, P. S.; Lyon, J. G.; Huete, A. (Eds.) 2012. Hyperspectral remote sensing of vegetation. Boca Raton, FL, USA: CRC Press. 705p.
Remote sensing ; Vegetation ; Indicators ; Multispectral imagery ; Satellite observation ; Satellite imagery ; Image analysis ; Data processing ; Data analysis ; Algorithms ; Models ; Sensors ; Water use ; Agriculture ; Crop management ; Cereal crops ; Cotton ; Botany ; Tissue analysis ; Nitrogen content ; Moisture content ; Plant diseases ; Pastures ; Indicator plants ; Species ; Canopy ; Forest management ; Tropical forests ; Wetlands ; Ecosystems ; Soil properties ; Land cover ; Reflectance
(Location: IWMI HQ Call no: 621.3678 G000 THE Record No: H044548)
(0.54 MB)
3 Chemin, Yann; Phuphak, S.; Asilo, S.; Hijmans, R. J. 2012. Determining spatial and temporal patterns of submergence in rice with MODIS satellite data. International Journal of Geoinformatics, 8(2):1-12.
Rice ; Crop management ; Canopy ; Remote sensing ; Satellite surveys ; Surface water ; Flooding ; Drought / Northeast Thailand / Philippines / Nueva Ecija Province
(Location: IWMI HQ Call no: e-copy only Record No: H044965)
(4.28 MB)
Rice submergence is the condition by which the water level rises above the rice crop canopy. In general, rice plant response to submergence is to elongate its shoots above the rising water level. This costs in energy and eventually has a direct impact in terms of reducing yields. A specific gene, called Sub1, when introgressed into popular rice varieties by Marker Assisted Back-crossing, nearly stops the natural elongation process and permits a given local rice variety to sustain submerged conditions for a generally recognized period of about 2 weeks. Plant breeders now look for well-identified and location-accurate submergence areas in order to disseminate such improved local rice varieties. Remote sensing is proposed to provide surface water maps at high temporal resolution, determining a percentage of occurrences of surface water for a given pixel. Occurrence is defined as the count of days of identified surface water within a given period, returned in a percentage on that period. Rice area maps and knowledge of crop calendars are proposed to add to the assessment of submergence prone areas in two study areas, the Northeastern Thailand and Nueva Ecija in North Central Philippines.
4 Slika, J. W. F.; Arroyo-Rodriguezb, V.; Aibac, S.-I.; Alvarez-Loayzad, P.; Alvese, L. F.; Ashton, P.; Balvanera, P.; Bastian, M. L.; Bellingham, P. J.; van den Berg, E.; Bernacci, L.; da Conceicao Bispo, P.; Blanc, L.; Bohning-Gaese, K.; Boeckx, P.; Bongers, F.; Boyle, B.; Bradford, M.; Brearley, F. Q.; Hockemba, M. B.-N.; Bunyavejchewin, S.; Matos, D. C. L.; Castillo-Santiago, M.; Catharino, E. L. M.; Chai, S.-L.; Chen, Y.; Colwell, R. K.; Robin, C. L.; Clark, C.; Clark, D. B.; Clark, D. A.; Culmsee, H.; Damas, K.; Dattaraja, H. S.; Dauby, G.; Davidar, P.; DeWalt, S. J.; Doucet, J.-L.; Duque, A.; Durigan, G.; Eichhorn, K. A. O.; Eisenlohr, P. V.; Eler, E.; Ewango, C.; Farwig, N.; Feeley, K. J.; Ferreira, L.; Field, R.; de Oliveira Filho, A. T.; Fletcher, C.; Forshed, O.; Franco, G.; Fredriksson, G.; Gillespie, T.; Gillet, J.-F.; Amarnath, Giriraj; Griffith, D. M.; Grogan, J.; Gunatilleke, N.; Harris, D.; Harrison, R.; Hector, A.; Homeier, J.; Imai, N.; Itoh, A.; Jansen, P. A.; Joly, C. A.; de Jong, B. H. J.; Kartawinata, K.; Kearsley, E.; Kelly, D. L.; Kenfack, D.; Kessler, M.; Kitayama, K.; Kooyman, R.; Larney, E.; Laumonier, Y.; Laurance, S.; Laurance, W. F.; Lawes, M. J.; do Amaral, I . L.; Letcher, S. G.; Lindsell, J.; Lu, X.; Mansor, A.; Marjokorpi, A.; Martin, E. H.; Meilby, H.; Melo, F. P. L.; Metcalfea, D. J.; Medjibe, V. P.; Metzger, J. P.; Millet, J.; Mohandass, D.; Montero, J. C.; de Morisson Valeriano, M.; Mugerwa, B.; Nagamasu, H.; Nilus, R.; Onrizal, S. O.-G.; Page, N.; Parolin, P.; Parren, M.; Parthasarathy, N.; Paudel, E.; Permana, A.; Piedade, M. T. F.; Pitman, N. C. A.; Poorter, L.; Poulsen, A. D.; Poulsen, J.; Powers, J.; Prasad, R. C.; Puyravaud, J.-P.; Razafimahaimodison, J.-C.; Reitsma, J.; dos Santos, J. R.; Spironello, W. R.; Romero-Saltos, H.; Rovero, F.; Rozak, A. H.; Ruokolainen, K.; Rutishauser, E.; Saiter, F.; Saner, P.; Santos, B. A.; Santos, F.; Sarker, S. K.; Satdichanh, M.; Schmitt, C. B.; Schongart, J.; Schulze, M.; Suganuma, M. S.; Sheil, D.; da Silva Pinheiro, E.; Sist, P.; Stevart, T.; Sukumar, R.; Sun, I.-F.; Sunderand, T.; Suresh, H. S.; Suzuki, E.; Tabarelli, M.; Tang, J.; Targhetta, N.; Theilade, I.; Thomas, D. W.; Tchouto, P.; Hurtado, J.; Valencia, R.; van Valkenburg, J. L. C. H.; Van Do, T.; Vasquez, R.; Verbeeck, H.; Adekunle, V.; Vieira, S. A.; Webb, C. O.; Whitfeld, T.; Wich, S. A.; Williams, J.; Wittmann, F.; Woll, H.; Yang, X.; Yao, C. Y. A.; Yap, S. L.; Yoneda, T.; Zahawi, R. A.; Zakaria, R.; Zang, R.; de Assis, R. L.; Luize, B. G.; Venticinque, E. M. 2015. An estimate of the number of tropical tree species. Proceedings of the National Academy of Sciences of the United States of America, 112(24):7472-7477. [doi: https://doi.org/10.1073/pnas.1423147112]
(Location: IWMI HQ Call no: e-copy only Record No: H047084)
5 Gago, J.; Douthe, C.; Coopman, R. E.; Gallego, P. P.; Ribas-Carbo, M.; Flexas, J.; Escalona, J.; Medrano, H. 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153:9-19. [doi: https://doi.org/10.1016/j.agwat.2015.01.020]
Water stress ; Water management ; Water use efficiency ; Sustainable agriculture ; Aerial photography ; Thermography ; Remote sensing ; Precision agriculture ; Crops ; Plant physiology ; Plant water relations ; Canopy ; Reflectance ; Chlorophylls ; Fluorescence
(Location: IWMI HQ Call no: e-copy only Record No: H047412)
(2.14 MB)
Unmanned aerial vehicles (UAVs) present an exciting opportunity to monitor crop fields with high spatial and temporal resolution remote sensing capable of improving water stress management in agriculture. In this study, we reviewed the application of different types of UAVs using different remote sensors and compared their performance with ground-truth plant data. Several reflectance indices, such as NDVI, TCARI/OSAVI and PRInorm obtained from UAVs have shown positive correlations related to water stress indicators such as water potential (_ ) and stomatal conductance (gs). Nevertheless, they have performed differently in diverse crops; thus, their uses and applications are also discussed in this study. Thermal imagery is also a common remote sensing technology used to assess water stress in plants, via thermal indices (calculated using artificial surfaces as references), estimates of the difference between canopy and air temperature, and even canopy conductance estimates derived from leaf energy balance models. These indices have shown a great potential to determine field stress heterogeneity using unmanned aerial platforms. It has also been proposed that chlorophyll fluorescence could be an even better indicator of plant photosynthesis and water use efficiency under water stress. Therefore, developing systems and methodologies to easily retrieve fluorescence from UAVs should be a priority for the near future. After a decade of work with UAVs, recently emerging technologies have developed more user-friendly aerial platforms, such as the multi-copters, which offer industry, science, and society new opportunities. Their use as high-throughput phenotyping platforms for real field conditions and also for water stress management increasing temporal and resolution scales could improve our capacity to determine important crop traits such as yield or stress tolerance for breeding purposes.
6 Biazin, B.; Haileslassie, Amare; Zewdie, T.; Mekasha, Y.; Gebremedhin, B.; Fekadu, A.; Shewage, T. 2018. Smallholders’ avocado production systems and tree productivity in the southern highlands of Ethiopia. Agroforestry Systems, 92(1):127-137. [doi: https://doi.org/10.1007/s10457-016-0020-2]
Agricultural production ; Fruit trees ; Avocados ; Smallholders ; Farmers ; Highlands ; Agroforestry ; Harvesting ; Canopy ; Coffee industry ; Land ownership ; Households / Ethiopia
(Location: IWMI HQ Call no: e-copy only Record No: H047783)
Ethiopia is one of the top five avocado producers in sub-Saharan Africa. Despite increasing recognition for its nutritional value and economic importance, information on smallholder avocado production systems across agro-climatic zones and determinants for tree productivity are literally lacking. Therefore, the objectives of this study were to examine the determinants for avocado tree holdings by smallholder farmers and investigate the effect of avocado production systems and management conditions on fruit yield by individual avocado trees in Southern Ethiopia. Data required for the study was collected through a combination of focus group discussions, household survey and field tree inventories. The data was analyzed using descriptive statistics, analyses of variance and linear regression methods using statistical software for social sciences (SPSS version 20). In the study region, avocado is mainly grown as an integral component of the coffee- and enset-based agroforestry systems. The number of avocado trees owned by smallholder producers was related to district, sex of household head, age of household head, educational status, land holding size, pest and disease damage and access to extension services. Productivity of avocado was significantly (p < 0.05) different between production systems. The highest avocado fruit yield was observed from trees grown in the coffee and enset-based agroforestry systems. However, the smallholder producers complain that the yields of coffee and enset grown under avocado trees could be very low. The total height of avocado trees was significantly (p < 0.05) different across the different production systems. The mean heights of matured (21–25 years old) avocado trees were 17.57 ± 0.86 m (±SE; N = 20) under coffee-based agroforestry system and 14.93 ± 1.24 m when grown as individual trees around homes. Proper extension support is needed to disseminate improved production techniques: encompassing proper tree spacing, tree training, pruning, soil amendments, growing optimum number of trees for successful pollination and improved harvesting.
7 Haileselassie, H.; Araya, A.; Habtu, S.; Meles, K. G.; Gebru, G.; Kisekka, I.; Girma, A.; Hadgu, K. M.; Foster, A. J. 2016. Exploring optimal farm resources management strategy for Quncho-teff (Eragrostis tef (Zucc.) Trotter) using AquaCrop model. Agricultural Water Management, 178:148-158. [doi: https://doi.org/10.1016/j.agwat.2016.09.002]
Crop management ; Eragrostis tef ; Irrigation water ; Water productivity ; Models ; Farm management ; Strategies ; Crop yield ; Fertilizer application ; Sowing date ; Soil water characteristics ; Chemicophysical properties ; Rain ; Biomass ; Canopy ; Experimentation / Ethiopia / Mekelle
(Location: IWMI HQ Call no: e-copy only Record No: H047852)
(1.52 MB)
Teff is a major staple food crop in Ethiopia. Moisture and soil fertility are the two major factors limiting teff yield. Studies were conducted across three sites in Ethiopa [Mekelle (MK) in 2012 and 2016, Ilala (IL) in 2012 and Debrezeit (DZ) in 2009 and 2010]. The objectives of these studies were (1) to assess the response of Quncho-teff to different fertilizer and irrigation levels; 2) to quantify irrigation water productivity (IWP), and (3) to collect data to calibrate and validate AquaCrop model for simulating yield and evaluate optimal irrigation and sowing date strategy for Quncho-teff at different locations in Ethiopia. The different fertilizer levels were: 1) 64 kg N and 46 kg P/ha (N2P2); 2); 32 kg N and 23 kg P/ha (N1P1); 3) 0 kg N and 0 kg P/ha (N0P0) and 4) 52 kg N and 46 kg P/ha (N3P3). The four irrigation treatments were: zero (rainfed), two, four and full irrigation applications. Findings showed that full irrigation in combination with high fertilizer (N2P2) could give better yield. However, during abnormal rainfall, spreading the available fertilizer at a rate of 32 kg N and 23 kg P/ha may be preferable to applying 64 kg N and 46 kg P/ha. This study also indicated that the regional fertilizer recommendations for teff need to be revised taking in to account the soil characteristics, climate and irrigation water availability. The AquaCrop model was able to simulate the observed canopy cover, soil water, biomass and yield of teff satisfactorily. Canopy cover was simulated with normalized root mean square error (NRMSE), index of agreement (I) and R2 of 7%, 0.5 and 0.8, respectively. Soil moisture during the season was simulated with NRMSE of 11.4–15.7%, I of 0.99 and R2 of 0.85–0.9. Simulated final aboveground biomass values were in close agreement with the measured (NRMSE, 7.8%, I, 0.89 and R2, 0.66). There was also good agreement between simulated and measured grain yield with NRMSE, I and R2 values of 10.9%, 0.93, 0.80, respectively. Scenario analysis indicated that early sowing was the best option to maximize teff yield with the least amount of irrigation. Scenario analysis also showed that one irrigation during flowering stage could substantially improve irrigation water productivity (IWP) of teff and minimize the yield loses which could occur due to shifting of sowing date from early to normal. Two irrigation applications also substantially improved the yield and IWP of late sown teff. However, to get high yield, a late sown teff should receive at least four irrigation applications during the mid-growth stage of the crop. These results suggest that AquaCrop model can be used to identify optimal farm resource management strategies for teff production.
8 Negussie, A.; Achten, W. M. J.; Norgrove, L.; Mekuria, Wolde; Hadgu, K. M.; De Both, G.; Leroy, B.; Hermy, M.; Muys, B. 2016. Initial effects of fertilization and canopy management on flowering and seed and oil yields of Jatropha curcas L. in Malawi. BioEnergy Research, 9:1231-1240. [doi: https://doi.org/10.1007/s12155-016-9767-6]
Fertilizer application ; Fertilization ; Nitrogen fertilizers ; Inorganic fertilizers ; Canopy ; Flowering ; Seed production ; Oilseeds ; Jatropha curcas ; Biofuels ; Bioenergy ; Agronomy ; Agronomic practices ; Pruning implements ; Planting ; Spacing ; Soil sampling / Malawi
(Location: IWMI HQ Call no: e-copy only Record No: H047879)
Appropriate canopy management, including planting density and pruning, and application of fertilizer may increase flowering success and seed and oil yields of Jatropha curcasL.Twofieldexperimentswereperformedfrom2009to 2011 in Balaka, Malawi, to assess the effect of planting density and pruning regime and single fertilizer application (N, P, and K) on male and female flower number and seed and oil yields of J. curcas. Planting density influenced flower sex ratio and female flower number. Branch pruning treatments did not influence the flower sex ratio but reduced seed and final oil yield by 55 % in the following year. It is claimed that J.curcas can be grown on soils with low nutrient content, but this study revealed that yield was low for non-fertilized trees. WeobservedhigherseedandoilyieldsathigherNapplication rates(upto203±42%seedand204±45%oilyieldincrease) compared with the non-fertilized control. The study suggests thatcurrentlyusedheavypruningpracticeisnotrecommended for J.curcas cultivation, although it needs further longer term investigation. Applying nitrogen fertilizer is effective in increasing yield.
9 Amarnath, Giriraj; Babar, S.; Murthy, M. S. R. 2017. Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India: a multi-scale satellite remote sensing approach. The Egyptian Journal of Remote Sensing and Space Sciences, 20:157-168. [doi: https://doi.org/10.1016/j.ejrs.2017.05.004]
Remote sensing ; Models ; Vegetation ; Satellite imagery ; Forest fragmentation ; Forest ecosystems ; Trees ; Canopy ; Time series analysis ; Deforestation ; Landscape ; Climate change / India
(Location: IWMI HQ Call no: e-copy only Record No: H048220)
http://www.sciencedirect.com/science/article/pii/S1110982317302132/pdfft?md5=272802c5ea945f049718e7f6501c83bf&pid=1-s2.0-S1110982317302132-main.pdf
https://vlibrary.iwmi.org/pdf/H048220.pdf
https://vlibrary.iwmi.org/pdf/H048220.pdf
(3.34 MB)
Monitoring the changes in forest-cover and understanding the dynamics of the forest is becoming increasingly important for the sustainable management of forest ecosystems. This paper uses temporal MODIS Vegetation Continuous Field (MODIS-VCF) to monitor the tree cover change in the Indian region over a period of 6 years (2000–2005). Pixel-based linear regression model is developed to identify rate of deforestation and fragmentation at landscape level. The regression parameters viz., slope, offset and variance are used to identify threshold between forest and non-forest classes. The classification algorithm resulted into change area, no change area, positive change and negative changes. MODIS-VCF raw product of 2005 was validated using the field data and showed a coefficient of determination (R2 = 0.85) between percent tree cover and individual plot wise canopy cover information. The results were overlaid with UNEP protected area boundary. On a long-term basis, the forest cover change was monitored using medium spatial resolution (Landsat and IRS) satellite data to identify the rate of deforestation and fragmentation at landscape level. The developed approach is efficient and effective for regional monitoring of forest cover change. It could be automated for regular usage and monitoring.
10 Bachiller-Jareno, N.; Hutchins, M. G.; Bowes, M. J.; Charlton, M. B.; Orr, H.G. 2019. A novel application of remote sensing for modelling impacts of tree shading on water quality. Journal of Environmental Management, 230:33-42. [doi: https://doi.org/10.1016/j.jenvman.2018.09.037]
Water quality ; Remote sensing ; Riparian vegetation ; Trees ; Canopy ; Rivers ; Surface water ; Water temperature ; Geographical information systems ; Models / England / River Thames
(Location: IWMI HQ Call no: e-copy only Record No: H049295)
(2.02 MB)
Uncertainty in capturing the effects of riparian tree shade for assessment of algal growth rates and water temperature hinders the predictive capability of models applied for river basin management. Using photogrammetry-derived tree canopy data, we quantified hourly shade along the River Thames (UK) and used it to estimate the reduction in the amount of direct radiation reaching the water surface. In addition we tested the suitability of freely-available LIDAR data to map ground elevation. Following removal of buildings and objects other than trees from the LIDAR dataset, results revealed considerable differences between photogrammetry- and LIDAR-derived methods in variables including mean canopy height (10.5 m and 4.0 m respectively), percentage occupancy of riparian zones by trees (45% and 16% respectively) and mid-summer fractional penetration of direct radiation (65% and 76% respectively). The generated data on daily direct radiation for 2010 were used as input to a river network water quality model (QUESTOR). Impacts of tree shading were assessed in terms of upper quartile levels, revealing substantial differences in indicators such as biochemical oxygen demand (BOD) (1.58–2.19 mg L-1 respectively) and water temperature (20.1 and 21.2 °C respectively) between ‘shaded’ and ‘non-shaded’ radiation inputs. Whilst the differences in canopy height and extent derived by the two methods are appreciable they only make small differences to water quality in the Thames. However such differences may prove more critical in smaller rivers. We highlight the importance of accurate estimation of shading in water quality modelling and recommend use of high resolution remotely sensed spatial data to characterise riparian canopies. Our paper illustrates how it is now possible to make better reach scale estimates of shade and make aggregations of these for use at river basin scale. This will allow provision of more effective guidance for riparian management programmes than currently possible. This is important to support adaptation to future warming and maintenance of water quality standards
11 Romero, J. M.; Cordon, G. B.; Lagorio, M. G. 2020. Re-absorption and scattering of chlorophyll fluorescence in canopies: a revised approach. Remote Sensing of Environment, 246:111860. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111860]
Plant physiology ; Chlorophylls ; Fluorescence emission spectroscopy ; Canopy ; Vegetation ; Crops ; Peas ; Maize ; Lolium ; Soils ; Remote sensing ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H049717)
(4.08 MB)
The measurement of chlorophyll fluorescence in remote way represents a tool that is becoming increasingly important in relation to the diagnosis of plant health and carbon budget on the planet. However, the detection of this emission is severely affected by distortions, due to processes of light re-absorption both in the leaf and in the canopy. Even though some advances have been made to correct the signal in the far-red, the whole spectral range needs to be addressed, in order to accurately assess plant physiological state. In 2018, we introduced a model to obtain fluorescence spectra at leaf level, from what was observed at canopy level. In this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment. In addition, multiple scattering between the soil and the canopy, and the fraction of land covered by vegetation have also been taken into consideration. We validate this model upon experimental measures, in three types of crops of agronomic interest (Pea, Rye grass and Maize) with different architecture. Our model accurately predicts both the shape of fluorescence spectra at leaf level from that measured at canopy level and the fluorescence ratio. Furthermore, not only do we eliminate artifacts affecting the spectral shape, but we are also able to calculate the quantum yield of fluorescence corrected for re-absorption, from the experimental quantum yield at canopy level. This represents an advance in the study of these systems because it offers the opportunity to make corrections for both the fluorescence ratio and the intensity of the observed fluorescence.
12 Kumar, N.; Adeloye, A. J.; Shankar, V.; Rustum, R. 2020. Neural computing modelling of the crop water stress index. Agricultural Water Management, 239:106259. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106259]
Crop water use ; Water stress ; Neural networks ; Models ; Evaluation ; Irrigation scheduling ; Soil moisture ; Temperature ; Wells ; Mustard ; Canopy / India / Hamirpur
(Location: IWMI HQ Call no: e-copy only Record No: H049750)
(3.26 MB)
In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.
13 Mwinuka, P. R.; Mbilinyi, B. P.; Mbungu, W. B.; Mourice, S. K.; Mahoo, H. F.; Schmitter, Petra. 2021. The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L). Agricultural Water Management, 245:106584. [doi: https://doi.org/10.1016/j.agwat.2020.106584]
Water stress ; Eggplants ; Canopy ; Water requirements ; Crop yield ; Forecasting ; Infrared imagery ; Multispectral imagery ; Unmanned aerial vehicles ; Remote sensing ; Irrigated farming ; Irrigation water ; Performance evaluation ; Moisture content ; Vegetation index ; Plant developmental stages ; Temperature / Africa / United Republic of Tanzania / Rudewa Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050054)
https://www.sciencedirect.com/science/article/pii/S0378377420321314/pdfft?md5=25877087dd8e72a2377978976c8abc33&pid=1-s2.0-S0378377420321314-main.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
(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.
14 Damm, A.; Cogliati, S.; Colombo, R.; Fritsche, L.; Genangeli, A.; Genesio, L.; Hanus, J.; Peressotti, A.; Rademske, P.; Rascher, U.; Schuettemeyer, D.; Siegmann, B.; Sturm, J.; Miglietta, F. 2022. Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy. Remote Sensing of Environment, 273:112957. (Online first) [doi: https://doi.org/10.1016/j.rse.2022.112957]
Plant water relations ; Leaf water potential ; Canopy ; Remote sensing ; Surface temperature ; Vegetation index ; Chlorophylls ; Fluorescence ; Soil water ; Maize / Italy / Tuscany
(Location: IWMI HQ Call no: e-copy only Record No: H050996)
https://www.sciencedirect.com/science/article/pii/S0034425722000712/pdfft?md5=f358a1acfb0c958d984037b09f412ce7&pid=1-s2.0-S0034425722000712-main.pdf
https://vlibrary.iwmi.org/pdf/H050996.pdf
https://vlibrary.iwmi.org/pdf/H050996.pdf
(10.80 MB) (10.8 MB)
Vegetation responds at varying temporal scales to changing soil water availability. These process dynamics complicate assessments of plant-water relations but also offer various access points to advance understanding of vegetation responses to environmental change. Remote sensing (RS) provides large capacity to quantify sensitive and robust information of vegetation responses and underlying abiotic change driver across observational scales. Retrieved RS based vegetation parameters are often sensitive to various environmental and plant specific factors in addition to the targeted plant response. Further, individual plant responses to water limitation act at different temporal and spatial scales, while RS sampling schemes are often not optimized to assess these dynamics. The combination of these aspects complicates the interpretation of RS parameter when assessing plant-water relations. We consequently aim to advance insight on the sensitivity of physiological, biochemical and structural RS parameter for plant adaptation in response to emerging soil water limitation. We made a field experiment in maize in Tuscany (Central Italy), while irrigation was stopped in some areas of the drip-irrigated field. Within a period of two weeks, we measured the hydraulic and physiological state of maize plants in situ and complemented these detailed measurements with extensive airborne observations (e.g. sun-induced chlorophyll fluorescence (SIF), vegetation indices sensitive for photosynthesis, pigment and water content, land surface temperature). We observe a double response of far-red SIF with a short-term increase after manifestation of soil water limitation and a decrease afterwards. We identify different response times of RS parameter representing different plant adaptation mechanisms ranging from short term responses (e.g. stomatal conductance, photosynthesis) to medium term changes (e.g. pigment decomposition, changing leaf water content). Our study demonstrates complementarity of common and new RS parameter to mechanistically assess the complex cascade of functional, biochemical and structural plant responses to evolving soil water limitation.
15 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.
16 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.
17 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.
18 Alvar-Beltran, J.; Saturnin, C.; Gregoire, B.; Camacho, J. L.; Dao, A.; Migraine, J. B.; Marta, A, D. 2023. Using AquaCrop as a decision-support tool for improved irrigation management in the Sahel Region. Agricultural Water Management, 287:108430. (Online first) [doi: https://doi.org/10.1016/j.agwat.2023.108430]
Decision support systems ; Irrigation management ; Tomatoes ; Maize ; Quinoa ; Food security ; Agricultural extension ; Water productivity ; Models ; Water resources ; Precipitation ; Evapotranspiration ; Drought stress ; Canopy ; Irrigation schemes ; Yields ; Crop water use ; Water requirements ; Early warning systems / Sahel / West Africa / Burkina Faso
(Location: IWMI HQ Call no: e-copy only Record No: H052111)
https://www.sciencedirect.com/science/article/pii/S0378377423002950/pdfft?md5=071f68c31d21c94884a3be995aaa27d5&pid=1-s2.0-S0378377423002950-main.pdf
https://vlibrary.iwmi.org/pdf/H052111.pdf
https://vlibrary.iwmi.org/pdf/H052111.pdf
(3.41 MB) (3.41 MB)
Operational systems providing irrigation advisories to agricultural extension workers are paramount, particularly in West Africa where the yield gap represents the greatest agriculture growth-led opportunity. The proposed framework for Burkina Faso, an irrigation decision support system (DSS), is based on in-situ weather and field observations necessary for feeding the atmosphere, soil, and crop modules of crop-water productivity models (e.g., AquaCrop). To optimize water resources, incoming irrigation and precipitation, and outgoing evapotranspiration are constantly monitored and adjusted. The findings of the proposed semi-automatic irrigation DSS indicate that water stresses affecting the canopy cover and stomatal closure are minimized if the proposed irrigation schemes are generated and improved with five-day weather observations. The source of uncertainty in crop models’ evapotranspiration estimations is reduced by systematically comparing the observed crop evapotranspiration (ETc) with historical ETc records. An increase in yields is observed in all studied crops, from 1960 to 2018 kg/ha (tomato dry yields), from 2571 to 2799 kg/ha (maize), and from 1279 to 1385 kg/ha (quinoa) when comparing the 2020–21 and 2021–22 experiments. Results show an optimization of water resources, with a higher evapotranspired water productivity (WPET, expressed as dry weight) when comparing the two experiments, from 0.86 to 0.97 kg/m3 for tomato, from 0.85 to 0.86 kg/m3 for maize, and from 0.67 to 0.73 kg/m3 for quinoa, respectively in 2020–21 and 2021–22. The proposed irrigation DSS can be used to inform extension workers and technical agronomic experts about real-time crop water requirements and, thus, assist the Climate Risk and Early Warning Systems (CREWS) initiative that aims to improve access to weather information for decision-support in agriculture. Afterwards, extension agents can catalyze irrigation advisories and support farmers improve irrigation management at the field level to, ultimately, obtain higher yields.
19 Yakob, G.; Habte, M.; Smith, J. U.; Hallett, P. D.; Phimister, E.; Rivington, M.; Black, H.; Mekuria, Wolde. 2023. Changes in soil properties with long-term organic inputs due to distance from homestead and farm characteristics in southern Ethiopian farmlands. Geoderma Regional, 35:e00710. [doi: https://doi.org/10.1016/j.geodrs.2023.e00710]
Soil properties ; Soil fertility ; Soil organic carbon ; Farmland ; Agricultural practices ; Canopy ; Cation exchange capacity ; Agricultural productivity ; Households ; Income ; Gender ; Women / Ethiopia / Halaba / Andegna Choroko / Lay Arisho / Asore
(Location: IWMI HQ Call no: e-copy only Record No: H052330)
https://www.sciencedirect.com/science/article/pii/S2352009423001062/pdfft?md5=d7dc9a6b182797b14078593359e2edaa&pid=1-s2.0-S2352009423001062-main.pdf
https://vlibrary.iwmi.org/pdf/H052330.pdf
https://vlibrary.iwmi.org/pdf/H052330.pdf
(4.95 MB) (4.95 MB)
Traditional farming systems across much of Sub-Saharan Africa have greater organic inputs near to the homestead than in fields further away. This is likely to produce a fertility gradient that impacts production capacity, and so provides an opportunity to explore impacts of organic amendments on soils. Across 198 farm plots in 69 households in Halaba, Southern Ethiopia, we investigated the influence of different organic input systems on soil properties. The study also examined the influence of household and farm characteristics on the adoption of land management practices and its impact on soil properties. Samples were taken from farm plots located close (300 m) from the homestead, representing different levels of organic amendments. Soils located close to homesteads had significantly greater soil organic carbon, cation exchange capacity and soil nutrient content compared to soil located near and far from the homestead areas. Soil organic carbon concentrations close to the home were 15%, 27% and 45% greater than farm plots located at far from the home in Andegna Choroko, Asore and Lay Arisho kebeles, respectively. Across all sites, the mean soil organic carbon stock ranged from 20.6 t ha- 1 to 84.6 t ha- 1 , depending on the location of the plots with respect to the homestead. Household and farm characteristics also influenced land management practices and soil properties. In some catchments, farm plots managed by female headed households and relatively rich farmers displayed significantly greater soil organic carbon than farm plots managed by male headed and relatively poor households. This was likely due to greater organic inputs in female headed households in areas where men were otherwise engaged in off-farm activities and in wealthier households with greater access to organic manures. Tree cover in farmlands influenced accumulation of soil organic carbon. The results suggest that out-scaling farm management practices that are common around homesteads, such as adding animal manure or household wastes and maintaining tree cover, would help to improve key soil properties and agricultural productivity.
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