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2 Mechanisms of Drought Resistance
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... Monitoring of weed species will provide great help to make a decision on applying suitable control methods (Minbashi Moeini et al., 2008). Weed distribution on fields is not uniform and limited to different size of patches on field (Weis et al., 2008) and since there is significant difference in weeds among different fields, site-specific weed monitoring and management are necessary (Moran et al., 2004). Weed researchers need to predict weed populations by developing models that help to: (i) estimate the presence of weeds on fields relating their population dynamics with the expenses and profits of their control, (ii) estimate the effects of new management before field implementation, and (iii) simulation weed populations responses to changes in the environment or managing. ...
This study was carried out in 2013 and 2014 to compare the potential of artificial neural networks and logistic regression to predict dominant weed presence on dryland chickpea and winter wheat fields in Kurdistan province, Iran. In both models, climatic and soil characteristics were defined as independent variables and presence/absence of the dominant weeds as the dependent variable. The geographical coordinates of each field was overlaid on georeferenced map of the province for producing the distribution of weed species maps in ArcGIS. Also, the zonation maps developed by using GIS based on LR models. Demographic indices of weed species were calculated, and the dominant weeds were determined. In the area under study, 61 and 74 weed species were identified on chickpea and winter wheat fields, respectively. The results indicated that Galium aparine L., Convolvulus arvensis L., Scandix pectin-veneris L. and Tragopogon graminifolius DC. at three-leaf stage (99, 81, 71 and 70, respectively), Convolvulus arvensis and Tragopogon graminifolius at podding stage of chickpea (96 and 77, respectively); and Convolvulus arvensis, Tragopogon graminifolius, Turgenia latifolia (L.) Hoffm. and Carthamus oxyacantha M. B. at heading stage of winter wheat (95, 80, 78 and 72, respectively) were the dominant weeds with the highest abundance indices. The logit models did not show good fitness and could not fit any models for Galium aparine at three leaf stage and dominant weeds at podding stage of chickpea. However, ANN could develop the best suited models for prediction all dominant weeds with high MSE values. Sensitivity analysis on the optimal networks revealed that altitude and rainfall were the most significant parameters. The results demonstrates the potential of ANN as a promising tool for survey of weed population dynamics.
... Site-specific weed management has reduced herbicide use by 11–90 % without affecting crop yield (Feyaerts and van Gool 2001; Gerhards and Christensen 2003). Weed distribution in fields is non-uniform and confined to patches of varying size in field as well as along field borders (Gerhards et al. 1997; Weis et al. 2008) and, since there is significant variation in weeds also between different fields, the need for site-specific weed monitoring and management is emphasized (Moran et al. 2004). Non-selective weed detection and control can be implemented by detection of green vegetation (Biller 1998 ). ...
Site-specific weed management can allow more efficient weed control from both an environmental and an economic perspective. Spectral differences between plant species may lead to the ability to separate wheat from weeds. The study used ground-level image spectroscopy data, with high spectral and spatial resolutions, for detecting annual grasses and broadleaf weeds in wheat fields. The image pixels were used to cross-validate partial least squares discriminant analysis classification models. The best model was chosen by comparing the cross-validation confusion matrices in terms of their variances and Cohen’s Kappa values. This best model used four classes: broadleaf, grass weeds, soil and wheat and resulted in Kappa of 0.79 and total accuracy of 85 %. Each of the classes contains both sunlit and shaded data. The variable importance in projection method was applied in order to locate the most important spectral regions for each of the classes. It was found that the red-edge is the most important region for the vegetation classes. Ground truth pixels were randomly selected and their confusion matrix resulted in a Kappa of 0.63 and total accuracy of 72 %. The results obtained were reasonable although the model used wheat and weeds from different growth stages, acquisition dates and fields. It was concluded that high spectral and spatial resolutions can provide separation between wheat and weeds based on their spectral data. The results show feasibility for up-scaling the spectral methods to air or spaceborne sensors as well as developing ground-level application.
... Weeds are the most acute pest in agriculture, with an estimated annual global damage of around 40 thousand million US dollars (USD) per year (Monaco, Weller, and Ashton 2002). Weeds reduce crop yield and quality by competing with crops for water, sunlight, and minerals (Pinter et al. 2003;Slaughter, Giles, and Downey 2008); producing allelopathic substances (Moran et al. 2004); hosting diseases and insects (Pikart et al. 2011;Papayiannis, Kokkinos, and Alfaro-Fernandez 2012); and disturbing tilling and harvesting (Monaco, Weller, and Ashton 2002). One increasing problem is weed resistance to herbicides (Mallory-Smith, Thill, and Dial 1990;Jones et al. 2005;Marshall and Moss 2008). ...
... Weed distribution in fields is non-uniform and confined to patches of varying size along field borders (Gerhards et al. 1997;Lamb and Brown 2001;Vrindts, De Baerdemaeker, and Ramon 2002;Gerhards and Christensen 2003;Moran et al. 2004;Slaughter, Giles, and Downey 2008;Weis et al. 2008). Application of herbicides on a field is often based on the previous year's weed problems and information obtained from field scouting (Manh et al. 2001;Moran et al. 2004). ...
... Weed distribution in fields is non-uniform and confined to patches of varying size along field borders (Gerhards et al. 1997;Lamb and Brown 2001;Vrindts, De Baerdemaeker, and Ramon 2002;Gerhards and Christensen 2003;Moran et al. 2004;Slaughter, Giles, and Downey 2008;Weis et al. 2008). Application of herbicides on a field is often based on the previous year's weed problems and information obtained from field scouting (Manh et al. 2001;Moran et al. 2004). By significantly reducing the quantity of herbicide applied (Gerhards et al. 1997;Gerhards and Christensen 2003;Timmermann, Gerhards, and Kuehbauch 2003;Eddy et al. 2006;Slaughter, Giles, and Downey 2008;Weis et al. 2008), site-specific weed control and management could economically benefit farmers and consumers, as well as the environment, without diminishing weed control efficacy (Pinter et al. 2003;Slaughter, Giles, and Downey 2008;Weis et al. 2008). ...
Weed control is commonly performed by applying selective herbicides homogeneously
over entire agricultural fields. However, applying herbicide only where needed could
have economical and environmental benefits. The objective of this study was to apply
remote sensing to the detection of grasses and broadleaf weeds among cereal and
broadleafcrops.Spectralrelativereflectancevaluesatbothleafandcanopyscaleswere
obtained by field spectroscopy for four plant categories: wheat, chickpea, grass weeds,
and broadleaf weeds. Total reflectance spectra of leaf tissues for botanical genera were
successfully classified by general discriminant analysis (GDA). The total canopy spectral
classification by GDA for specific narrow bands was 95± 4.19% for wheat and
94± 5.13% for chickpea. The total canopy spectral classification by GDA for future
Vegetation and Environmental Monitoring on a New Micro-Satellite (VENµS) bands
was 77±8.09% forwheat and 88± 6.94%forchickpea, and forthe operative satellite
Advanced Land Imager (ALI) bands was 78± 7.97% for wheat and 82± 8.22% for
chickpea. Within the critical period for weed control, an overall classification accuracy
of 87± 5.57% was achieved for>5% vegetation coverage in a wheat field, thereby
providing potential for implementation of herbicide applications. Qualitative models
based on wheat, chickpea, grass weed, and broadleaf weed spectral properties have
high-quality classification and prediction potential that can be used for site-specific
weed management.
... This is the region where a sharp change in reflectance between wavelengths 690 and 750 nm takes place, and characterizes the transition from chlorophyll absorption to leaf scattering (Clevers et al., 2002). It has been demonstrated that the shape of the red-edge region is strongly influenced by LAI (Delegido et al., 2008;Herrmann et al., 2011;Lee et al., 2004) principally by the slope of the reflectance curve in this region (Filella and Peñuelas, 1994), while an increase in leaf chlorophyll content causes a shift in the red-edge position towards longer wavelengths (Dash and Curran, 2004;Filella and Peñuelas, 1994;Herrmann et al., 2011;Moran et al., 2004). ...
Leaf area index (LAI) is a key biophysical parameter for the monitoring of agroecosystems. Conventional two-band vegetation indices based on red and near-infrared relationships such as the normalized difference vegetation index (NDVI) are well known to suffer from saturation at moderate-to-high LAI values (3-5). To bypass this saturation effect, in this work a robust alternative has been proposed for the estimation of green LAI over a wide variety of crop types. By using data from European Space Agency (ESA) campaigns SPARC 2003 and 2004 (Barrax, Spain) experimental LAI values over 9 different crop types have been collected while at the same time spaceborne imagery have been acquired using the hyperspectral CHRIS (Compact High Resolution Imaging Spectrometer) sensor onboard PROBA (Project for On-Board Autonomy) satellite. This extensive dataset allowed us to evaluate the optimal band combination through spectral indices based on normalized differences. The best linear correlation against the experimental LAI dataset was obtained by combining the 674 nm and 712 nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and the red-edge position region, respectively, and are known to be sensitive to the physiological status of the plant. Contrary to the NDVI (r(2): 0.68), the red-edge NDI correlated strongly (r(2): 0.82) with LAI without saturating at larger values. The index has been subsequently validated against field data from the 2009 SEN3EXP campaign (Barrax, Spain) that again spanned a wide variety of crop types. A linear relationship over the full LAI range was confirmed and the regression equation was applied to a CHRIS/PROBA image acquired during the same campaign. A LAI map has been derived with an RMSE accuracy of 0.6. It is concluded that the red-edge spectral index is a powerful alternative for LAI estimation and may provide valuable information for precision agriculture, e.g. when applied to high spatial resolution imagery.
... It can be seen that the moisture parameter reliability is low, just as in the wheat experiment. It is possible that by using a larger variety of remote sensing data collection equipment, such as sensing in thermal bands (Moran et al., 2004), may help to increase the significance of the results. ...
a b s t r a c t This paper presents a remote sensing model for crop monitoring that was developed by the authors in a multi-year study. It also presents two experiments conducted for testing a newly developed application. The model combines remote sensing models using mapping of the spatial distribution of vegetation in an agricultural field, with precision agricultural models that maximize the output (yield) while minimizing the input (cost). This combination enables one to operate a monitoring and management process that includes every sub-unit of the field using remote sensing mapping. The model consists of five steps: (1) Preparing information layers that map the crop-affecting elements, e.g. irrigation and topography; (2) Collecting spectral and plant data simultaneously; (3) Processing and analyzing the data in order to prepare vegetation maps; (4) Decision-making in accordance with the above-mentioned maps or with predicted-yield maps; and (5) Quality control. The experiments showed that although the results were not statistically significant, the application of the proposed model enables one to draw recommendations within 45 h, and that remote sensing monitoring results in more benefits than do traditional control methods. The quality control was not ideal, due to the narrow range of the spectrum used in the remote sensing monitoring.
... However, these methods have not been shown to be sufficiently sensitive to be used for irrigation scheduling. Techniques for remote sensing of crop water stress have included determination of canopy temperature [10] and vegetation indices that use red and near-infrared reflectance [11,12]. ...
... NDWI measures plant water, mostly held in canopy foliage, as a different physical property, i.e., liquid water instead of vapor. It has been successfully demonstrated to map vegetative water content (VWC) from satellites on a daily basis [7,11,12]. ...
In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.
... Chlorophyll content, which can be roughly related to nitrogen content, recently became one of the key variables for measuring plant canopies in precision farming. That farming concept takes into account intra-field variability and ultimately aims at applying the appropriate amounts of inputs, fertilizers for instance, in various locations within a field to achieve an economically profitable yield while preserving the environment (Moran et al., 2004). The monitoring of water content is also subject to special attention in agriculture because this variable limits productivity of crop plants exposed to water stress. ...
The capability for in situ measurements of leaf biochemistry is very useful for applications in agriculture, forestry or ecology. A portable field radiometer, RAMIS (RAdiomètre portatif de Mesure In Situ) has been developed by the University of Paris 7 − Denis Diderot for this purpose. Unlike the SPAD-502 (Minolta) and CCM-200 (Opti-Sciences) devices which were only designed to determine total chlorophyll concentration, RAMIS should additionally estimate the leaf equivalent water thickness and the leaf mass per area (or specific leaf area) by measuring leaf transmittance at five wavelengths in the VIS-NIR-MIR and inversion of the PROSPECT leaf optical properties model. In order to validate it, a collaborative field campaign organized in June 2003 in the INRA of Angers (France) led us to build a database, gathering 324 leaf samples: 222 maple (Acer pseudoplatanus L.) leaves coming from trees grown under glass in variable environmental conditions (light intensity, nitrogen content, water supply, etc.) and 102 leaves of about forty different species collected outdoors. The chlorophyll concentration, equivalent water thickness and leaf mass area were determined for each sample by classical methods. In parallel, radiometric measurements have been performed using RAMIS, SPAD-502, and a portable field spectrophotometer, FieldSpec-FR (Analytical Spectral Devices).
This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts. Two plots of barley grown under rainfed conditions in Spain were monitored during the growing cycle of 2015 (February to June). The dataset included nine field estimations of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were retrieved and integrated to derive the six agronomic and monitoring variables, including the height, biomass, fraction of vegetation cover, leaf area index, water content, and soil moisture. The statistical methods applied, namely double smoothing, ARIMAX, and robust regression, allowed the adjustment and modelling of these field variables. The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop’s development, as occurs in natural conditions. After combining different models, the results showed the best efficiency in terms of forecasting and the influence of several weather variables. The existence of a cointegration relationship between the data series of the same crop in different fields allows for adjusting and predicting the results in other fields with similar crops without re-modelling.
Monitoring of irrigated land cover is important for both resource managers and farmers. An operational approach is presented to use the satellite-derived surface temperature and vegetation cover in order to distinguish between irrigated and non-irrigated land. Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of surface temperature and vegetation cover was achieved for each irrigation period (Spring, Summer and Autumn). The three periodic profiles were used to define irrigation land covers from 2008–2009 to 2018–2019 in a key agricultural region of Australia. The overall accuracy of identifying farms with irrigated land cover amounted to 95.7%. Total irrigated land cover was the lowest (approximately 200,000 ha) in the 2008–2009 crop year which increased more than three-fold in 2012–2013, followed by a gradual decline in the following years. Satellite images from Landsat series (L-5, L-7 and L-8), Sentinel-2 and ASTER were found suitable for land cover classification, which is scalable from farm to regional levels. For this reason, the results are desirable for a range of stakeholders.
Leaf Area Index (LAI) governs canopy processes. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of Sentinel-2 for assessing LAI. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The continuous data were resampled to Sentinel-2 resolution. The LAI prediction abilities by Partial Least Squares (PLS) models were compared and evaluated. For the continuous and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93 and 0.92, respectively. According to the Variable Importance in Projection (VIP) analysis, the red-edge spectral region was found to be highly important for LAI assessment. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed. The prediction abilities of these indices were compared, peaking for wheat, with REIP r values of 0.91 for both data formations. Therefore, it is concluded that Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat and therefore can be potentially implemented by sensors containing four red-edge bands.
