Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

Sensing soil and foliar phosphorus fluorescence in <Emphasis Type="Italic">Zea mays</Emphasis> in response to large phosphorus additions

Additions of large loads of phosphorus (P) enriched animal manure to soils and the persistence of their environmental impact have been associated with continued water quality impairment in regions of high density of confined animal feeding operations. Foliar P in corn (Zea mays L.) and changes in labile P in Aquic Hapludults were determined following P application of 0–560 kg P ha^?1 as KH₂PO₄ and an application of Fe³⁺ (150 mg Fe³⁺ kg^?1) in field mini-lysimeters to develop calibrations of soil and plant nutritional responses. X-ray fluorescence (XRF) scanning of uppermost leaves of plants at the V2, V5, and V8 stages showed that foliar P proportionally increased with addition rates. Exchangeable and enzyme-labile P forms were effective indicators of foliar XRFS-P for up to 30 days after emergence. Phosphorus calibration curves developed for flag leaves showed that spatial distribution of foliar P (3.6, 4.2, and 5.3 g kg^?1) corresponded to field zones treated with 0, 15, and 30 kg P ha^?1 as dairy manure P for the past 18 years. Up-to-date crop uptake and availability of P in these Hapludults were best described by a square root function of soil XRFS-P and total exchangeable inorganic P (r² = 0.4; RMSE = 419 and 422 g ha^?1, respectively). Therefore, a timely knowledge of canopy P status and its linkage to actual soil P status supports in situ element-specific sensing and precision nutrient management in order to manage the declining use-efficiency in crops and reduce potential loss to the environment.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker? active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha^?1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r ² = 0.65). This study shows that the GreenSeeker? active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.     

Estimation of the rootzone depth above a gravel layer (in wild blueberry fields) using electromagnetic induction method

Wild blueberry (Vaccinium angustifolium Ait.) fields in the north east Canada are naturally grown in a course textured thin layer of soil and below this layer is a soilless layer of gravel. The root zone depth of this crop varies from 10 to 15 cm. Investigating the depth to the gravel layer below the course textured soil is advantageous, as it affects the water holding capacity of the root zone. Water and nutrient management are the two primary determinants of crop yield and the amount of leaching. The objective of this study was to estimate the depth to the gravel layer using DualEM-2 instrument. A C++ program written in Visual Studio 2010 was used to develop mathematical models for estimating the depth to the gravel layer from the outputs of DualEM-2 sensor. Two wild blueberry fields were selected in central Nova Scotia, Canada to evaluate the performance of DualEM-2 instrument in estimating the rootzone depth above the gravel layer. The mid points of squares created by grid lines were used as the sampling points at each experimental site. The actual depth to the interface was measured manually at selected grid points (n = 50). The apparent ground conductivity (EC_a) values of DualEM-2 were recorded and the depth to the interface was estimated for the same sampling points within the selected fields. The fruit yield samples were also collected from the same grid points to identify the impact of the depth to the gravel layer on crop yield. After calibrations, comprehensive surveys were conducted and the actual and estimated depths to the interface were established. The interpolated maps of fruit yield, and the actual (z_in) and estimated (\( {\text{z}}_{\text{in}}^{*} \)) depths to the interface were created in ArcGIS 10 software. Results indicated that the z_in was significantly correlated with \( {\text{z}}_{\text{in}}^{*} \) for the North River (R ² = 0.73; RMSE = 0.27 m) and the Carmel (R ² = 0.45; RMSE = 0.20 m) sites. Results revealed that the areas with shallow depth to the gravel layer were low yielding, indicating that the variation in the depth to the gravel layer can have an impact on crop productivity. Non-destructive estimations of the depth to the gravel layer can be used to develop erosion control strategies, which will result in an increased crop production.     

Evaluating the performance of xanthophyll,chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (g_s), stem water potential (Ψ_s) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R₇₀₀/R₆₇₀), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI_(570–515)] and the normalized PRI (PRI_norm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψ_s yielded the best correlations with PRI and PRI_(570–515) (r² = 0.53) in almond trees, with TCARI/OSAVI (r² = 0.88) in apricot trees and with PRI_norm, R₇₀₀/R₆₇₀ and NDVI (r² from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (T_c) and PRI_(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψ_s was the most sensitive indicator at midday. PRI_norm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI_(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, g_s and, particularly, Ψ_s (r² = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).     

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP₀) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP₀ and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP₀ and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R² = 0.66 and 0.70, respectively) and Crop Circle (best R² = 0.71 and 0.77, respectively) sensors worked well for estimating YP₀ and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R² = 0.82) and green ratio vegetation index (R² = 0.73) explained 26 and 22 % more variability in YP₀ and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP₀ or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP₀ and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP₀ at later growth stages.     

Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass

Whole farm evaluations have shown that accurate yield data are difficult to collect for alfalfa (Medicago sativa L.) and grass mixtures and corn (Zea mays L.) silage fields. Additionally, on-farm research, a recommended tool for adaptive management, is hindered by lack of practical ways to collect yield data. Recently, forage yield monitors have become available on self-propelled forage harvesters (SPFHs), but precision and accuracy of this technology are unknown. The objective of this project was to evaluate accuracy of yield and moisture sensing components of forage yield monitors for use in alfalfa/grass and corn silage. Moisture content, mass flow weights, total area harvested and total dry yield per hectare were measured on 11 farms in 2013; forage samples were collected for truck loads, analyzed for dry matter content, and compared to monitor-registered dry matter. Truck weights were used to compare monitor-derived yield to actual yield on two farms for alfalfa/grass and three farms for corn silage. Moisture sensors estimated crop moisture content within 3.7 % DM for alfalfa/grass and 3.0 % DM for corn silage of the oven dry value. Flow sensors estimated alfalfa/grass yield to ±0.5 and ±1.1 Mg DM/ha for corn silage. When calibrations are done regularly, forage yield monitors can provide an accurate and precise measure of dry yield for adaptive management. It is concluded that this technology can be used when plots are large and large treatment-driven yield differences are expected.     

Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis

Protein content, which represents rice taste quality, must be estimated in order to create a harvesting plan as well as next year’s basal dressing fertilizer application plan. Ground-based hyperspectral imaging with high resolution (1 × 1 mm per pixel) was used for estimating the protein content of brown rice before harvest. This paper compares the estimation accuracy of rice protein content estimation models generated from the mean reflectances of five regions of interest (ROIs): the overall target area, dark area (less illuminated parts of the rice plants), canopy area (leaves, yellow leaves, and ears), leaf area, and ear and yellow leaf area. The size of the target sampling area was 0.85 × 0.85 m. An R + G + B histogram and a GNDVI–NDVI image were used to separate the target area into the individual ROIs. The values of the coefficient of determination R ² and the root mean square error of prediction (RMSE) were similar for each model: R ² ranged from 0.83 to 0.86 and RMSE ranged from 0.27 to 0.30% for all models except for the dark area model, where R ² = 0.76 and RMSE = 0.35%. There were no significant differences in the magnitude of the estimation error among all models. This result indicates that it is not necessary to obtain an image with a ground resolution that is greater than 0.85 × 0.85 m per pixel to estimate rice protein content before harvest. This result should provide useful information when deciding the altitude of platforms for imaging rice fields.     

Probability of profitable yield response to nitrification inhibitor used with liquid swine manure on corn

Nitrification inhibitors (NI) can be used with liquid swine manure (LSM) to decrease potential NO₃ losses, but knowledge specifying when and where NI can increase corn (Zea mays L.) yields is limited. Eleven on-farm evaluation trials (OET) were conducted in 2009 and 15 in 2010 to identify site-specific factors for using Instinct (an encapsulated form of nitrapyrin) with LSM in Iowa. Farmers injected LSM in the fall in at least three field-long strips with and without NI. Yield responses (YR) to NI were calculated by dividing yield monitor data into 50-m cells within each field. Hierarchical models were used to estimate predictive probabilities of profitable YR for two categories of monthly average rainfall and soil drainage. On average, NI produced no YR in relatively normal 2009 and a 0.15 Mg ha^?1 YR in extremely wet 2010. The NI did not change late-season corn N status but half of corn stalk nitrate test (CSNT) samples were N deficient in 2009 and about 65 % in 2010. Fields receiving >90 cm March through August rainfall in 2010 were predicted 65 % more likely to have economic YR (>0.13 Mg ha^?1) than fields receiving <90 cm rainfall. Within-field variability in YR was about four times greater than among-field variability, but within field-level factors had no significant effects on YR. The NI effects may not have lasted long enough to increase yields across all OET and predictive probabilities suggest that NI may produce profitable YR only when spring and summer rainfall exceed the long-term averages by more than 40 %.     

Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data

Water productivity (WP) is a key element of agricultural water management in agricultural irrigated regions. The objectives of this study were: (i) to estimate biomass of winter wheat using spectral indices; (ii) integrate the estimation of biomass data with the AquaCrop model using a lookup table for higher accuracy biomass simulation; (iii) show estimation accuracy of the data assimilation method in yield and WP. Spectral variables and concurrent biomass, yield and WP of samples were acquired at the Xiaotangshan experimental site in Beijing, China, during the 2008/2009, 2009/2010, 2010/2011 and 2011/2012 winter wheat growing seasons. The results showed that all spectral indices had a highly significant relationship with biomass, especially normalized difference matter index, with R² and RMSE values of 0.84 and 1.43 t/ha, respectively. Simulation of biomass and yield by the AquaCrop model were in good agreement with the measured biomass and yield of winter wheat. The results showed that the data assimilation method (R² = 0.79 and RMSE = 0.12 kg/m³) could be used to estimate WP. The result indicated that the AquaCrop model could be used to estimate yield and WP with the aid of remote sensing for improving agricultural water resources management.     

Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρ_soil), chlorophyll content (C_a+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and C_a+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R ² = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R ² = 0.89 (RMSE = 0.64) and R ² = 0.56 (RMSE = 0.61), respectively.     

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat

When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R² = 0.49, while the current model without a soil moisture adjustment had an R² = 0.40.     

Spectral indices from aerial images and their relationship with properties of a corn crop

Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha^?1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).     

Optimization of corn plant population according to management zones in Southern Brazil

Precision agriculture relies on site-specific interventions determined by the spatial variability of factors driving plant growth. The main objective of this study was to assess the efficiency of variable-rate seeding of corn (Zea mays L.) with delineated management zones. This study involved two experiments carried out in Não-Me-Toque, Rio Grande do Sul, Brazil. For the first experiment, carried out in 2009/2010, management zones were delineated by the farmer’s knowledge of the crop field. The field was split into low (LZ), medium (MZ) and high (HZ) crop performance zones. In the second experiment, carried out in 2010/2011, management zones were delineated by overlaying standardized yield data from nine crop seasons (seven of soybean and two of corn). The experiment was carried out with a randomized block design with three management zones and five corn seeding rates ranging from 50 000 to 90 000 seeds per ha^?1. The soil was a Rhodic Hapludox with a subtropical climate. Optimization of the corn plant population within the field increased grain yield compared to the reference plant population (70 000 plants ha^?1). Yield increases in the LZ, due to corn plant population reduction in relation to the target population, were 1.20 and 1.90 Mg ha^?1 for first and second experiments, respectively. This resulted in economic gains of 19.8 and 28.7 %, respectively. Yield increases in the HZ were 0.89 and 0.94 Mg ha^?1, respectively, and were due to an increase in plant population in relation to the target population. This resulted in economic gains of 5.6 and 6.6 % for the first and second experiments, respectively. In the MZ, the adjustment of the target plant population was not necessary. Optimizing corn population according to management zones is a promising tool for precision agriculture in Southern Brazil.     

Simulating field-scale variability and precision management with a 3D hydrologic cropping systems model

Effective variable-rate nitrogen (N) management requires an understanding of temporal variability and field-scale spatial interactions (e.g. lateral redistribution of nutrients). Modeling studies, in conjunction with field data, can improve process understanding of agricultural management. CropSyst-Microbasin (CS-MB) is a fully distributed, 3-dimensional hydrologic cropping systems model that simulates small (10 s of hectares) heterogeneous agricultural watersheds with complex terrain. This study used a highly instrumented 10.9 ha watershed in the Inland Pacific Northwest, USA, to: (1) assess the accuracy of CS-MB simulations of field-scale variability in water transport and crop yield in comparison to observed field data, and (2) quantify differences in simulated yield and farm profitability between variable-rate and uniform fertilizer applications in low, average and high precipitation treatments. During water years 2012 and 2013 (a “water year” refers to October 1st through the following September 30th, where a given water year is named for the calendar year on September 30th), the model simulated surface runoff with a Nash–Sutcliffe efficiency (NSE) of 0.7, periodic soil water content (comparison to seasonal soil core measurements) with a root mean square error (RMSE) ≤0.05 m³ m^?3, and continuous soil water content (comparison to in situ soil sensors) at 15 of 20 microsites with NSE ≥0.4. The model predicted 2013 field variability in winter wheat yield with RMSE of 1100 kg ha^?1. Simulated uniform N management resulted in 0–35 kg ha^?1 greater field average yield in comparison to variable-rate management. The savings in fertilizer costs under variable-rate N management resulted in $23–$32 ha^?1 greater field average returns to risk. This study demonstrated the capacity of CS-MB to further understanding of simulated and observed field-scale spatial variability and simulated crop response to low, medium and high annual precipitation.     

Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat

Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350–2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R² = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R² = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing.     

Comparison of passive and active canopy sensors for the estimation of vine biomass production

Recent advances in optical designs and electronic circuits have allowed the transition from passive to active proximal sensors. Instead of relying on the reflectance of natural sunlight, the active sensors measure the reflectance of modulated light from the crop and so they can operate under all lighting conditions. This study compared the potential of active and passive canopy sensors for predicting biomass production in 25–32 randomly selected positions of a Merlot vineyard. Both sensors provided estimates of the normalized difference vegetation index (NDVI) from a nadir view of the canopy at veraison that were good predictors of pruning weight. Although the red NDVI of the passive sensors explained more of the variation in biomass (R ² = 0.82), its relationship to pruning weight was nonlinear and was best described by a quadratic regression (NDVI = 0.55 + 0.50 wt−0.21 wt²). The theoretically greater linearity of the amber NDVI-biomass relationship could not be verified under conditions of high biomass. The linear correlation to stable isotope content in leaves (¹³C and ¹⁵N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited fertilizer N uptake. Thus, the canopy reflectance data provided by these mobile sensors can be used to improve site-specific management practices of vineyards.