Abstracts

Csillag, F., Kertész, M., et al. 2001, 'On the Measurement of Diversity-Productivity Relationships in a Northern Mixed Grass Prairie (Grasslands National Park, Saskatchewan, Canada)', Community Ecology, vol. 2, no. 2, pp. 145-159.

For the investigation of diversity-productivity relationships under natural conditions, we present an operationally feasible measurement scheme explicitly considering the spatial organization of vegetation. We hypothesised that the spatial arrangement of the coexistence of species influences patch-level productivity. To characterise diversity, co-occurrences of species were recorded along oval transects allowing scaling by aggregation between 5 cm and 25 m. Productivity was characterised by field radiometric measurements, calibrated for leaf area and biomass, arranged in a sampling scheme scalable between 20 cm and 50 m. All data were collected along a slight resource gradient in the Stipa-Bouteloua (upland) community of the northern mixed-grass prairie in Grasslands National Park, Saskatchewan. We found a wide range of correlations (Kendall’s τ between -0.2 and 0.9) between various measures of diversity (species richness, local species combinations) and productivity (average and variability of leaf-area index) as a function of sampling unit size. For field assessment of patch-level composition and functioning, we recommend to use samples at the spatial resolution corresponding to the maximum number of local species combinations as an appropriate scale for comparison. We demonstrate how our sampling methodology can be considered for possible process-oriented inference about diversity and productivity. To characterise diversity-productivity relationships for long-term monitoring and prediction of plant community structure and functioning, scalable, repeatable, non-destructive observations should be applied.

Request Information                              Back to Reference List

Dudka, M., Langton, S., et al. 1998, Use of Digital Imagery to Evaluate Disease Incidence and Yield Loss Caused by Sclerotinia Stem Rot of Soybeans, Proc. of the 1998 International Precision Agriculture Conference, St. Paul, MN. pp. 9 pp.

Remotely sensed spectral data were used to assess the incidence of Sclerotinia stem rot of soybean caused by the fungus Sclerotinia sclerotiorum and to determine its effect on variability of soybean yields. Multispectral data were obtained with an ATLAS sensor (Airborne Terrestrial Applications Sensor), yields were mapped with a combine mounted yield monitor, and field disease assessments made both visually and by means of spectral reflectance observations obtained with a handheld radiometer. Limitations in data obtained during the ground truth survey prevented use of multispectral data for disease assessment. However, our results indicate that disease incidence and crop yield can be estimated from spectral reflectance data, that plant disease can explain a high percentage of yield variability in a production soybean field, and that diseased areas can be mapped using precision agricultural techniques. This information will enable growers to utilize variable rate technologies to control Sclerotinia stem rot.

Request Information                              Back to Reference List

Elwadie, M.E., Pierce, F.J., et al. 2005, 'Remote Sensing of Canopy Dynamics and Biophysical Variables Estimation of Corn in Michigan', Agronomy Journal, vol. 97, pp. 99-105.

Remotely sensed data can aid in estimating biophysical variables of corn (Zea mays L.). This study identifies spectral wavelengths, spectral vegetation indices (SVIs), and timing needed for estimating yield and leaf area index (LAI) for corn. Canopy reflectance (460–810 nm range) was measured periodically in 1999 and 2000 within a field study varying N and irrigation management for corn. Corn grain yield was strongly related to canopy reflectance for either individual wavelengths or for SVIs, reaching an optimum (R² > 0.9) at R5 dent stage in both years. Green reflectance based on simple ratio (green simple ratio index, GSRI) had the highest R², lowest RMSE, and most consistent slope and intercept between years. In contrast, LAI was best predicted by normalized difference vegetation index (NDVI) (RSME = 0.426) while green normalized difference vegetation index (GNDVI) performed poorly (RMSE = 0.604). Corn grain yield in this study was best predicted at stage R5 using the green simple ratio index.

Request Information                              Back to Reference List

Green, D.E., Burpee, L.L., et al. 1998, 'Canopy Reflectance as a Measure of Disease in Tall Fescue', Crop Science, vol. 28, pp. 1603-1613.

Measurement of changes in canopy reflectance of tall fescue (Festuca arundinacea Schreb.), which result from deterioration of tissues caused by disease organisms, is a potential unbiased method for quantifying disease. Canopy reflectance in eight spectral bands between 430 and 840 nm and 18 vegetation indices derived from the spectral bands were regressed against visual severity estimates of Rhizoctonia blight, caused by Rhizoctonia solani Kuhn, and gray leaf spot, caused by Pyricularia grisea (Cooke) Sacc. Reflectance within the 810-nm band exhibited the strongest relationship (19% ≤ r² ≤ 63%) with visual severity estimates of Rhizoctonia blight and gray leaf spot. Reflectance in the visible or near infra-red wavelengths was similar to tall fescue blighted by either fungal pathogen. A significant (P ≤ 0.05) negative linear relationship was observed between canopy reflectance in the 810-nm band and the severity of either Rhizoctonia blight or gray leaf spot. As Rhizoctonia blight decreased with increases in the application rate of the fungicide flutolanil (N-[3-(1-methylethoxy)- phenyl]-2-(trifluoromethyl)benzamide) and the proportion of a resistant cultivar in tall fescue blends, canopy reflectance in the 810-nm band increased. However, models based on canopy reflectance had twice as much unexplained variability than models based on visual severity estimates of Rhizoctonia blight. Factors other than disease influenced variability in reflectance of light from tall fescue canopies.

Request Information                              Back to Reference List

Heidmann, T., Thomsen, A., et al. 2000, 'Modelling Soil Water Dynamics in Winter Wheat using Different Estimates of Canopy Development', Ecological Modelling, vol. 129, pp. 229-243.

Three years of soil water dynamics within plots of winter wheat were measured and simulated by the SOIL model. The winter wheat was cultivated at three nitrogen fertilization levels (0, 60 and 120 kg N ha^-1), resulting in differences in canopy development and evaporative demand between plots. The soil properties were assumed to be uniform within the field. The SOIL model was calibrated using soil water content measured during 1990 in a plot fertilized at the highest level. Standard meteorological variables together with measured parameters describing soil and plant properties were used as inputs to the model. Additional parameters were obtained from the literature. The parameter set resulting from the calibration was applied for the years 1990–1992 and for all three fertilization levels. Measurements of leaf-area index and estimated rooting depth were specified for the individual years and plots. Time series of canopy resistance are usually supplied to the model as a driving variable, but in our application, the model alternatively calculated canopy resistance using the Lohammar equation. The equation has mostly been applied to forests but was here used for winter wheat with good results using parameter values obtained from the literature. Three estimates of canopy development were used as input to the SOIL model: green leaf-area index and green leaf + stem area index measured on plant samples in the laboratory and leaf area derived indirectly from measurements of spectral reflectance. The agreement between model predictions and measurements of soil water dynamics was generally good when green leaf-area index or leaf area derived from spectral-reflectance measurements were used as input. Generally, spectrally derived leaf area was found suitable for replacing laboratory measurements. Spectral reflectance measurements are non-destructive, fast and inexpensive compared to standard destructive measurements. Model predictions were most sensitive to the methods used for measuring leaf area index and for estimating canopy resistance during the early season when evapotranspiration was limited by canopy size and under drought and nitrogen limited conditions.

Request Information                              Back to Reference List

Li, H., Lascano, R.J., et al. 2001, 'Multispectral Reflectance of Cotton Related to Plant Growth, Soil Water and Texture, and Site Elevation', Agronomy Journal, vol. 93, pp. 1327-1337.

Radiometric data can be useful to determine the impact of field heterogeneity, irrigation, and fertilization on plant water and N use. A 2-yr (1998–1999) study was conducted on the South Texas High Plains to investigate cotton (Gossypium hirsutum L.) spectral and agronomic responses to irrigation and N fertilization and to determine the simple and cross correlation among cotton reflectance, plant growth, N uptake, lint yield, site elevation (SE), and soil water and texture. The treatments were irrigation at 50 and 75% of calculated cotton evapotranspiration (ET) and N rates of 0, 90, and 135 kg ha^-1 arranged in an incomplete block of size-2 design. Plant and soil spectral properties were investigated within a wavelength of 447 to 1752 nm. Near-infrared (NIR) reflectance was positively correlated with plant biomass and N uptake. Reflectance in the red and midinfrared band increased with SE. The mixed-model analysis showed that cotton NIR reflectance, normalized difference vegetative index (NDVI), soil water, N uptake, and lint yield were significantly affected by irrigation (P < 0.0012). The N treatment had no effect on spectral parameters, and interaction between irrigation and N fertilizer was significant on NIR reflectance (P < 0.0027). All spectral and agronomic parameters measured were associated with SE. The red and NIR reflectance and NDVI were cross-correlated with soil water, sand, clay, and SE across a distance of 60 to 80 m. Characterization of plant and soil reflectance and their spatial structure can be the basis for variable N application on heterogeneous fields to increase N use efficiency.

Request Information                              Back to Reference List

Ma, B.L., Dwyer, L.M., et al. 2001, 'Early Prediction of Soybean Yield from Canopy Reflectance Measurements', Agronomy Journal, vol. 93, no. 6, pp. 1227-1234.

Correlations between plant canopy reflectance and above ground biomass can possibly be used for early prediction of crop yield. Field experiments were conducted in 1998 and 1999 on two soil types to assess whether measurements of canopy reflectance at given stages of development could be used for screening and predicting soybean [Glycine max (L.) Merr.] yield in a variety trial. A 3 by 42 factorial experiment, arranged in a randomized complete block design with three replications, was used on each soil type for both years. Three population densities (25, 50 and 75 seeds m^-2) represented low, optimum, and high levels. Forty-two historical varieties represented nearly six decades (1934-1992) of soybean yield and improvement in Canada. Canopy reflectance was measured with a hand-held multispectral radiometer for each site. Grain yield at harvest was measured. Soybean grain yield was highly positively correlated with canopy reflectance, expressed as normalized difference vegetation index (NDVI), at all sampling dates. Regression analyses showed a positive relationship between NDVI and grain yield, with R² up to 0.80 (P < 0.01) and progressive improvement from R2 to R5 stages. Population density did not affect the yield-NDVI relationship at the development stages studied. Our data suggest that canopy reflectance measured nondestructively between R4 and R5 stahes adequately discriminates high- from low-yielding genotypes and provides a reliable, fast, repeatable indicator for screening and ranking soybean genotypes based on the relationship between NDVI and grain yield (R² ranged from 0.44-0.80)

Request Information                              Back to Reference List

Ma, B.L., Subedi, K.D., et al. 2005, 'Comparison of Crop-Based Indicators with Soil Nitrate Test for Corn Nitrogen Requirement', Agronomy Journal, vol. 97, pp. 462-471.

Received for publication May 18, 2004. Nitrogen amendment based on soil mineral N content before planting is unreliable in humid regions. A field experiment was conducted for 3 yr to (i) determine the appropriate rates and timing of N applications in the humid environment of eastern Ontario, Canada (45°23' N, 75°43' W); (ii) evaluate the ability of nondestructive plant-based methods compared with presidedress soil nitrate concentration test in discriminating fertilization N rates near sidedress time; and (iii) document how yearly variations in environmental conditions affect the ability of different approaches to assess corn (Zea mays L.) N status. Two hybrids were grown under eight combinations of rates and timing of N application in a factorial experiment. Leaf greenness and canopy reflectance were simultaneously measured from the V5 to V8 stages and at three occasions thereafter. Plant total N and soil available N (NO₃^- and NH₄⁺) at V6 were analyzed. Relationships of parameters collected early in the growing season vs. grain yield, harvest index, and total plant N uptake at maturity were determined. In 2 yr (2000 and 2002), grain yields increased significantly with fertilizer rates up to 120 kg N h^a–1. While soil mineral N and plant N concentrations differentiated 0 N from preplant N at 40 kg N ha^–1, both leaf chlorophyll and canopy reflectance measured at V6 stage responded linearly to fertilizer N up to 120 kg N ha^–1. We concluded that these leaf and canopy optical measurements could be used as crop-based indicators for early-season N amendment.

Request Information                              Back to Reference List

Nebeker, T.E. and Evans, D.L., 2000, Determination and Demonstration of Remote Sensing Capabilities in Detecting and Monitoring Defoliation, Mortality and Disturbances Over Forested Landscapes RSTC Mississippi State University pp 10 pp.

Fieldwork for 2000 began with the establishment of experimental plots at a privately owned cottonwood plantation in Leflore County, Mississippi. Small plots, consisting of a block of four trees, and large plots, consisting of a block of 16 trees, were installed on May 9, 2000. In total, we installed 15 small plots and 12 large plots. Plot corners were marked and mapped with a GPS unit. Individual trees within plots were also tagged and numbered. A total of 252 trees were tagged.
Trees in treatment plots were subjected to 25%, 50%, or 75% defoliation. We maintained undefoliated plots as controls for comparison. In addition, within our small plots, we established singletree plots that consisted of one representative of each treatment level (1 25%, 1 50%, and 1 75%) and control. Treatments were applied randomly to both small and large plots with treatments and control replicated three times for each plot size.
Artificial defoliation treatments were applied May 25, 2000 and again on September 15, 2000. Defoliation of trees within experimental plots was carried out using an instrument we designed that roughly mimics the sort of damage feeding CLB inflict on cottonwood terminals and leaves. The instrument consists of a 3.5 ft pvc pipe with four 1.6 ft. nylon cords tied at the top. At the end of each nylon cord wad tied a four-tined treble hook. A total of 184 trees were artificially defoliated.
Multispectral images of this area were captured on June 6, 2000 at an altitude of 4500 ft and on June 7 and June 24, 2000 at an altitude of 12,000 ft. Images of the area were again captured during the fall at 6000 ft. We had requested the plots be flown at 3,000 ft. for this first year of investigation. Reflectance data was also collected from randomly selected trees within all treatment and control plots using a hand-held multispectral radiometer. Radiometer data was collected one, two, and three weeks post-treatment during each treatment period.
In our preliminary analysis, wavelengths 760 nm and 810 seemed the most reliable in differentiating controls from defoliation treatments. In most cases, reflectance for control trees was significantly higher than for defoliated trees. Differentiation among the individual defoliation treatments was somewhat less clear. Overall, the 75% defoliation treatment typically exhibited significantly lower reflectance values than the other treatments. Lower levels of defoliation generally grouped together. These investigations will be intensified during 2001.

Request Information                              Back to Reference List

Olson, K.C., Cochran, R.C., et al., 1995, Estimation of Forage Production Using Multispectral Radiometry Proceedings from KSU Range Field Day, October 27, 1995 Manhattan, KansasCooperative Extension Service, Kansas State University pp 115-118

A sixteen-channel multispectral radiometer (MSR; Model 87, Cropscan ® , Inc.) was used to estimate forage production on native tallgrass prairie pastures during the 1992 - 1995 growing seasons. Approximately 550 plots (1.71 ft²) were hand-clipped at multiple stages of plant maturity ranging from late boot to dormancy in order to measure actual forage production. Plots were scanned prior to clipping using an MSR fitted with filters for measuring reflected sunlight at eight discrete bandwidths (range = 460 to 810 nm). Reflectance characteristics of tallgrass prairie forage were used to predict forage productivity with the aid of a neural network computer program. Forage productivity estimates made using MSR data were found to be closely correlated to those determined by hand clipping (r² = 0.70) across all plant growth stages and maturity levels. Determining forage production with multispectral radiometry in combination with a neural network is a reliable alternative to hand clipping and can be accomplished in much less time.

Request Information                              Back to Reference List

Tarr, A.B., Moore, K.J., et al. 2005, 'Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition', Crop Science, vol. 45, pp. 996-1003.

Pasturelands are inherently variable. It is this variability that makes sampling as well as characterizing an entire pasture difficult. Measurement of plant canopy reflectance with a ground-based radiometer offers an indirect, rapid, and noninvasive characterization of pasture productivity and composition. The objectives of this study were (i) to determine the relationships between easily collected canopy reflectance data and pasture biomass and species composition and (ii) to determine if the use of pasture reflectance data as a covariate improved mapping accuracy of biomass, percentage of grass cover, and percentage of legume cover across three sampling schemes in a central Iowa pasture. Reflectance values for wavebands most highly correlated with biomass, percentage of grass cover, and percentage of legume cover were used as covariates. Cokriging was compared with kriging as a method for estimating these parameters for unsampled sites. The use of canopy reflectance as a covariate improved prediction of grass and legume percentage of cover in all three sampling schemes studied. The prediction of above-ground biomass was not as consistent given that improvement with cokriging was observed with only one of the sampling schemes because of the low amount of spatial continuity of biomass values. An overall improvement in root mean square error (RMSE) for predicting values for unsampled sites was observed when cokriging was implemented. Use of rapid and indirect methods for quantifying pasture variability could provide useful and convenient information for more accurate characterization of time consuming parameters, such as pasture composition.

Request Information                              Back to Reference List

Vigier, B. 2001, 'Spatial Analysis of White Mold Infection in Soybean using Canopy Reflectance. Abstracts, The Canadian Phytopathological Society Annual Meeting, London, Ontario', Canadian Journal of Plant Pathology, vol. 23, pp. 194-210.

The present study investigates the use of canopy reflectance ratios and geostatistical methods for the detection of spatial disease spread of the common white mold in soybeans, caused by Sclerotinia sclerotiorum (Lib.) de Bary. Plots were inoculated with propagules of S. sclerotiorum and compared to control plots in field experiments carried out in 1999 and 2000. A white mold severity index was calculated using the number of diseased plants in 3 m x 3 m quadrats at harvest in 2000, a year with high infection levels. A canopy reflectance algorithm associated with the presence of this disease was developed, which could be validated on a similar trial done in 1999, a year with lower infection levels. The best reflectance algorithm to detect infected areas was measured at the R3-R4 growth stage in mid-August. The log-transformed value of the mean reflectance ratio measured between 545 and 575 nm (Cropscan^TM channel No. 3) over the mean reflectance between 445 and 475 nm (channel No. 1) was a successful algorithm to detect plant stress induced by early white mold infection. The study demonstrates that disease spread caused by white mold can be monitored from canopy reflectance at the end of the soybean vegetative stage and has potential for early detection of the disease.

Request Information                              Back to Reference List

Vrindts, E., Reyniers, M., et al. 2003, 'Analysis of Soil and Crop Properties for Precision Agriculture for Winter Wheat', Biosystems Engineering, vol. 85, no. 2, pp. 141-152.

In a precision farming research project financed by the Belgian Ministry of Small Trade and Agriculture, the methods of precision agriculture are tested on grain fields with a view of implementation of precision agriculture methods in Belgian field agriculture. The project encompasses methods for automatic information gathering on soil and crop and analysis of this data for management of within-field variability. Austomatic information capturing is combined with traditional data sources of soil sample analysis and crop observations. The measurements and part of the results on one particular field in Sauveniere are presented here. Five nitrogen management strategies were compared, but the resulting differences in nitrogen dose were small and did not lead to significantly different yield results. The yield results were correlated to topography-related variations in soil texture and chemical components and to crop reflectance measurements in May.

Request Information                              Back to Reference List

Xue, L., Cao, W., et al. 2004, 'Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance', Agronomy Journal, vol. 96, no. 1, pp. 135-142.

Nondestructive monitoring and diagnosis of plant N status is necessary for precision N management. The present study was conducted to determine if canopy reflectance could be used to evaluate leaf N status in rice (Oryza sativa L.). Ground-based canopy spectral reflectance and N concentration and accumulation in leaves were measured over the entire rice growing season under various treatments of N fertilization, irrigation and plant population. Analyses were made on the relationships of seasonal canopy spectral reflectance, ratio indices, and normalised difference indices to leaf N concentration and N accumulation in rice under different N treatments. The results showed that at each sampling date, leaf N concentration was negatively related to the reflectance at the green band (560 nm) while positively related to ratio index, with the best correlation at jointing. However, the relationships between leaf N accumulation and reflectance at green band and ratio index were consistant across the whole growth period. The ratio of near infrared (NIR) to green (R₈₁₀/R₅₆₀) was especially linearly related to the total leaf N accumulation, independant of N level and growth stage. Tests of the linear regression model with different field experiment data sets involving different plant densities, N fertilization and irrigation treatments exhibited good agreement between the predicted and observed values, with an estimation accuracy of 96.69%, root mean square error of 0.7072, and relative error of 0.0052. There results indicate that the ratio index of NIR to green (R₈₁₀/R₅₆₀) should be useful for nondestructive monitoring of N status in rice plants.

Request Information                              Back to Reference List