15 research outputs found

    An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images

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    Stable stressors on crops (e.g., salts, heavy metals), which are characterized by stable spatial patterns over time, are harmful to agricultural production and food security. Satellite data provide temporally and spatially continuous synoptic observations of stable stress on crops. This study presents a method for identifying rice under stable stress (i.e., Cd stress) and exploring its spatio-temporal characteristics indicators. The study area is a major rice growing region located in Hunan Province, China. Moderate-resolution imaging spectroradiometer (MODIS) and Landsat images from 2008–2017 as well as in situ measurements were collected. The coupling of a leaf canopy radiative transfer model with the World Food Study Model (WOFOST) via a wavelet transform isolated the effects of Cd stress from other abrupt stressors. An area wavelet transform stress signal (AWTS), based on a time-series Enhanced Vegetation Index (EVI), was used to detect rice under Cd stress, and its spatio-temporal variation metrics explored. The results indicate that spatial variation coefficients (SVC) of AWTS in the range of 0–1 ha d a coverage area greater than 70% in each experimental region, regardless of the year. Over ten years, the temporal variation coefficients (TVC) of AWTS in the range of 0–1 occurred frequently (more than 60% of the time). In addition, the Pearson correlation coefficient of AWTS over two consecutive years was usually greater than 0.5. We conclude that a combination of multi-year satellite-derived vegetation index data with a physical model simulation is an effective and novel method for detecting crops under environmental stress. A wavelet transform proved promising in differentiating between the effects of stable stress and abrupt stress on rice and may offer a way forward for diagnosing crop stress at continental and global scales

    A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

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    There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

    Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

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    In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

    High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

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    Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

    Characterization and modeling of water flow in sandy soils for irrigation optimization

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    Benefits and trade-offs of legume-led crop rotations on crop performance and soil erosion at various scales in SW Kenya

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    Soil erosion and land fragmentation threaten agricultural production in large parts of the Western Kenyan Highlands. In Rongo watershed, maizecommon bean intercropping systems, which dominate the agricultural landscape, are vulnerable to soil degradation, especially on long slope lengths where ground and canopy cover provision fail to protect the soil from the disruptive impact of raindrops. The inclusion of soil conservation measures like hedgerows, cover crops or mulch can reduce soil erosion, but compete with crops for space and labour. Knowledge of critical slope length can minimise interventions and tradeoffs. Hence, we evaluated maizecommon bean intercrop (MzBn) regarding runoff, erosion and crop yield in a slope length trial on 20, 60 and 84 m plot lengths, replicated twice on three farms during one rainy season in Rongo, Migori County. Additionally, we investigated systems of MzBn (farmers practice), MzBn with 5 Mg ha-1 Calliandra calothyrsus mulch (Mul), Arachis hypogaea (Gnt), Lablab purpureus (Lab) and Mucuna pruriens (Muc), regarding their impact on infiltration, runoff, soil loss, soil C and N loss during three rainy seasons (long and short rains, LR and SR, 2016, and LR 2017). Measured field data on soil, crop, spatial maps and meteorology were used as input datasets to parameterize and calibrate the LUCIA model. The calibrated and validated model was then used to simulate agronomic management scenarios related to planting date (planting with first rain vs baseline) and vegetation cultivar (short duration crop) to mitigate water stress. Based on the measurements, groundcover was most influential over rainfall intensity (EI30) and plant canopy cover in predicting soil loss. Dense groundcover of Mul at the beginning of the rainy seasons was decisive to significantly (p 5mm) in the topsoil under Mul at the end of SR 2016 significantly (p<0.05) increased infiltration rates (420 mm hr-1) in LR 2017 compared to Lab (200 mm hr-1) and Gnt (240 mm hr-1). Average C and N concentrations in eroded sediments were significantly reduced under Mul (0.74 kg C ha1, 0.07 kg N ha1) during the LR 2016 as compared to MzBn (3.20 kg C ha1, 0.28 kg N ha1) and Gnt (2.54 kg C ha1, 0.23 kg N ha1). Likewise, in SR 2016 Mul showed significantly lowered C and N losses of 3.26 kg C ha1 and 0.27 kg N ha1, respectively, over Lab (9.82 kg C ha1, 0.89 kg N ha1). Soil loss over 84 m slope length was overall significantly higher by magnitudes of 250 and 710% than on 60 and 20 m long plots, respectively, which did not differ significantly among each other (p<0.05). For runoff, 84 m plot length differed significantly from 60 and 20 m, but in the opposite trend as for soil loss. Across all three farms, slope gradient and slope length were the variables with highest explanatory power to predict soil loss. At the individual farm level, under homogeneous slope and texture, slope length and profile curvature were most influential. Considering results of slope length experiments, plot lengths less than 50 m appear to be preferential considering soil loss, sediment load, and soil loss to yield ratio under the given rainfall, soil and slope conditions. Our results call for integrating slope length options and cropping systems for effective soil conservation. We recommend planting Mucuna and Calliandrahedgerows as buffer strips below the critical slope length, and legume cash crops and maize uphill. Such approaches are critical in the backdrop of land fragmentation and labour limitation in the region to sustainably maximise land area. In the modelling exercise, crops planted one and three weeks after the baseline planting date increased Maize and Muc grain yield over the baseline during the three cropping seasons, the three weeks treatment in particular. This could be due to more favourable weather conditions during the shifted vegetation period. Increased grain yield corresponded to high water use efficiency (WUE). The short duration crop planted three weeks after the baseline planting date (PD3WL+SDC10) showed the highest grain yield after PD3WL (three weeks late plaing with BL variety). The use of cultivars with short growth cycle offers the flexibility of planting again where crops failed due to crop water stress or where the rains delay, ensuring completion of the growth cycle before the season ends. Given that short growth duration crops produce less grain yield compared to their counterpart full season crops, due to the length of their cycles, breeding programs must prioritize traits that can enhance the size of the grain-filling sink. At the plot level, management systems that reduce evaporation and retain soil moisture, e.g. mulching, application of farmyard manure etc., must be promoted to reduce evapotranspiration.Bodenerosion und Kleinteiligkeit von Betriebsflächen bedrohen die landwirtschaftliche Produktion in weiten Teilen des westkenianischen Hochlands. Im untersuchten Wassereinzugsgebiet von Rongo sind die weit verbreiteten Mais-Bohne-Mischkkultursysteme gefährdet durch Bodendegradierung. Dies ist vor allem auf langen Hängen und dort der Fall, wo der Oberboden nicht durch entsprechende Bodenbedeckung vor Schlagregen geschützt ist. Bodenschutzmaßnahmen wie Hecken, Bodendecker oder Mulch können das Ausmaß von Bodenerosion verringern, konkurrieren aber oft mit der Hauptkultur um Raum bzw. Arbeitskraft. Der gezielte Einsatz solcher Interventionen ausschliesslich in Bereichen kritischer Hangpositionen kann solcherlei Aufwand und Konkurrenzeffekte minimieren. In diesem Zusammenhang wurden in der hier vorgestellten Studie Mais-Bohne-Mischkulturen (MzBn) während einer Anbausaison auf drei unterschiedlichen Hanglängen (20, 60 und 84 m) mit jeweils zwei Wiederholungen auf drei Betrieben in Rongo, Migori County, hinsichtlich Oberflächenabfluss, Erosion und Ertrag verglichen. Zudem wurden MzBn, MzBn mit 5 Mg ha-1 Calliandra calothyrsus Mulch (Mul), Arachis hypogaea (Gnt), Lablab purpureus (Lab) und Mucuna pruriens (Muc) hinsichtlich Infiltration, Oberflächenabfluss, Erosion, organischem Boden-C und Gesamt-Boden-N während dreier Anbauperioden (lange und kurze Regenzeit 2016 und lange Regenzeit 2017) verglichen. Gemessene Boden- und Pflanzenparameter sowie Boden-, Landnutzungskarten und ein digitales Höhenmodell wurden nebst tagesgenauen Wetterdaten als Eingaben für das Lucia (Land Use Change Impact Assessment)-Modell verwendet. Mit dem kalibrierten und validierten Modell wurden dann Szenarien zum Wasserstressmanagement mit Fokus auf Aussaatzeitpunkten und Sortenwahl (verschiedene Vegetationsdauer) getetstet. Die Auswertung der Feldversuche zeigte, dass der Grad der Bodenbedeckung (durch Biomasse, Mulch und Streu) stärkeren Einfluss auf Bodenabtrag hatte als Regenintensität (EI30) und Bodenbedeckung des Blätterdachs allein. Die dichte Bodenbedeckung durch Calliandramulch in Mul zu Beginn der Saison war dabei entscheidend für signifikant geringeren Oberflächenabfluss (88, 87 und 84% niedriger als in MzBn, Lab und Gnt) und Bodenabtrag (66 und 65% niedriger als in Gnt und Lab). Der hohe Anteil großer Bodenaggregate > 5mm im Oberboden zum Ende der kurzen Regenzeit (SR) 2016 stand in Zusammenhang mit im Vergleich zu Lab (200 mm hr-1) and Gnt (240 mm hr-1) signifikant erhöhten Infiltrationsraten unter Mul (420mm h-1) in der langen Regenzeit (LR) 2017. Durchschnittliche C- und N-Konzentrationen in Sedimenten waren in der LR 2016 unter Mul (0.74 kg C ha1, 0.07 kg N ha1) signifikant niedriger als unter MzBn (3.20 kg C ha1, 0.28 kg N ha1) und Gnt (2.54 kg C ha1, 0.23 kg N ha1). Ebenso waren in der SR 2016 C- und N-Verluste deutlich geringer als unter Lab (3.26 kg C ha1 und 0.27 kg N ha1 im Vergleich zu 9.82 kg C ha1 und 0.89 kg N ha1). Bodenabtrag bei 84 m Hanglänge war 250 bzw. 710% höher als auf den 60 und 20 m Anlagen, wobei sich letztere statistisch (p<0.05) nicht unterschieden. Hinsichtlich Oberflächenabfluss unterschieden sich die Hanglängen ebenfalls statistisch, aber in entgegengesetzter Richtung. Im Vergleich der Flächen auf allen drei Betrieben waren Hangneigung und länge die statistisch einflussreichsten Faktoren bezüglich Bodenabtrag. Auf den einzelnen Betrieben, d.h. bei gleich Hangneigung und Bodenart, waren Hanglänge und Hangform ausschlaggebend. Als Ergebnis der Hanglängenversuche erwies sich eine Länge von 50 m unter den gegebenen Wetter-, Boden- und Geländebedingungen als kritisch bzgl. Erosion, Sedimentmengen und dem Verhältnis von Erosion zu Ertrag. Die Ergebnisse dieser Studie legen nahe, dass effektiver Bodenschutz vor allem durch die Integration von Hanglänge und Anbausystem (Pflanzenwahl) erreicht werden kann. Es wird empfohlen Calliandra-Hecken mit Mucuna-Unterpflanzung als Pufferzonen in Streifen unterhalb der kritischen Hanglänge anzulegen sowie Körnerleguminosen und Mais als cash crops oberhalb. Durch diesen Ansatz kann vor dem Hintergrund der Landfragmentierung und Knappheit an Arbeitskraft in der Untersuchungsregion die nutzbare Landfläche nachhaltig optimiert werden. Der Modellierungsteil dieser Studie zeigte, dass Erträge bei einer und besonders bei drei Wochen späterem Aussaatzeitpunkt im Vergleich zum lokal üblichen Termin während aller drei Anbauperioden zu höheren Kornerträgen führte. Grund hierfür könnten günstigere Wetterbedingungen während der somit verschobenen Vegetationsperiode sein. Die höheren Erträge gingen einher mit effizienterer Wassernutzung der Pflanzen. Eine Sorte mit verkürzter Vegetationsperiode, drei Wochen nach dem üblichen Termin gepflanzt (PD3WL+SDC10), erzielte die höchsten Erträge. Sorten kürzerer Vegetationsdauer bieten allgemein höhere Flexibilität in Fällen spät einsetzender Regenfälle oder von Pflanzenmortalität, da auch bei wiederholter Aussaat die Regenzeit noch hinreichend genutzt werden kann. Angesichts der niedrigereren Ertragbildung während verkürzter Vegetationsdauer sollte ein höherer Kornanteil prioritäres Zuchtziel für zukünftige Sorten sein. Auf der Seite der Landwirte bedeutet dies, dass vermehrt Anbausysteme, die Evaporation verringern und Bodenfeuchte konservieren (z.B. Mulchen, Mistgaben), zur Anwendung kommen sollten

    Proceedings of the European Conference on Agricultural Engineering AgEng2021

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    This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

    Sustainable Land Use and Rural Development in Southeast Asia: Innovations and Policies for Mountainous Areas

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    Sustainable Development; Landscape/Regional and Urban Planning; Agricultur

    African Handbook of Climate Change Adaptation

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    This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced
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