Relationship Between Coefficient of Variation Measured by Spectral Reflectance and Plant Density at Early Growth Stages in Winter Wheat

ABSTRACT

The use of by-plot coefficient of variation (CV) has not been evaluated in precision agricultural work. This study evaluated the relationship between CVs determined from normalized-difference vegetative index (NDVI) sensor readings, plant population, and sensing direction on NDVI values. Randomly selected plots, measuring 1 m² (2003) and 3 m² (2004), were established for this study. Plots in 2004 were divided into three 1 m² subplots with, 0 and 120 kg ha^?1 fall-applied N, and 80 kg ha^?1 topdress nitrogen (N). Sensor reading of subplots were taken at Feekes 5 and 7 using the Green Seeker hand-held sensor. Results showed that the relationship between vegetative RI (RI_NDVI) and harvest RI (RI_Harvest) improved with increasing CV values. The prediction of RI_Harvest was improved when CV was integrated into the RI_NDVI calculation. RI_Harvest can be better predicted with RI_NDVI when the CV of spectral radiance measurements is used in the RI_NDVI equation.     

Utilization of Existing Technology to Evaluate Spring Wheat Growth and Nitrogen Nutrition in South Dakota

Abstract

Sensor‐based technologies for in‐season application of nitrogen (N) to winter wheat (Triticum aestivum L.) have been developed and are in use in the southern Great Plains. Questions arise about the suitability of this technology for spring wheat production in the northern Great Plains. A field experiment was established in Brookings, SD, to evaluate the GreenSeeker Hand Held optical sensor (NTech Industries, Ukiah, CA) for predicting in‐season N status on three spring wheat cultivars (Ingot, Oxen, and Walworth) across five N treatments. Nitrogen rates were 0, 34, 68, 102, and 136 kg N ha^?1 applied preplant as ammonium nitrate. Sensor readings and plant biomass samples were collected at Feekes 6 and Feekes 10 growth stages. The sensor measures reflectance in the red and near infrared (NIR) regions of the electromagnetic spectrum. A normalized difference vegetation index (NDVI) was calculated. The ability of the sensor readings to predict biomass, plant N concentration, and plant N uptake for each sampling date was determined. In general, biomass, plant N concentration, and N uptake increased with increasing N rate for both sampling dates. Readings collected at Feekes 6 and Feekes 10 showed a significant relationship with plant biomass, N concentration, and N uptake for all varieties. Plant N uptake and NDVI resulted in a higher regression coefficients compared to biomass and plant N concentration for all varieties. Results suggest that existing sensor‐based variable nitrogen technology developed for winter wheat could be utilized in the northern Great Plains for estimating in‐season N need for spring wheat.     

Effect of row spacing,growth stage,and nitrogen rate on spectral irradiance in winter wheat

Soil reflectance affects spectral irradiance measurements taken in winter wheat at early stages of growth when percent cover is low. The objective of this study was to determine the critical percent vegetation coverage needed for forage nitrogen (N) uptake calibration with indirect spectral irradiance measurements. Two field experiments were conducted at Tipton and Perkins, OK in October 1996. The effect of row spacing (15.2, 19.0, 25.4, and 30.5 cm) and growth stage (Feekes 4 and 5) under various N fertilizer rates (0, 56, 112, and 168 kg N ha^‐1) on spectral irradiance measurements from wheat was evaluated. The normalized difference vegetative index (NDVI) was used to characterize wheat canopy irradiance. In general, NDVI decreased with increasing row spacing and increased with N fertilizer rate at Feekes growth stage 4. Row spacing and N rate were independent of each other since no significant interaction was found. High correlation (r=0.81–0.98) was observed between NDVI and vegetation coverage. Percent vegetation coverage was a good predictor of the other dependent variables including forage dry matter, and total N uptake, which could indirectly be determined using NDVI. The coefficients of variation (CV's) from NDVI values decreased with increasing vegetation coverage suggesting that less variable NDVI values (CV less than 10%) might be obtained from plots where vegetation coverage exceeds 50%.     

Optical Sensor‐Based Algorithm for Crop Nitrogen Fertilization

Abstract

Nitrogen (N) fertilization for cereal crop production does not follow any kind of generalized methodology that guarantees maximum nitrogen use efficiency (NUE). The objective of this work was to amalgamate some of the current concepts for N management in cereal production into an applied algorithm. This work at Oklahoma State University from 1992 to present has focused primarily on the use of optical sensors in red and near infrared bands for predicting yield, and using that information in an algorithm to estimate fertilizer requirements. The current algorithm, “WheatN.1.0,” may be separated into several discreet components: 1) mid‐season prediction of grain yield, determined by dividing the normalized difference vegetative index (NDVI) by the number of days from planting to sensing (estimate of biomass produced per day on the specific date when sensor readings are collected); 2) estimating temporally dependent responsiveness to applied N by placing non‐N‐limiting strips in production fields each year, and comparing these to the farmer practice (response index); and 3) determining the spatial variability within each 0.4 m² area using the coefficient of variation (CV) from NDVI readings. These components are then integrated into a functional algorithm to estimate application rate whereby N removal is estimated based on the predicted yield potential for each 0.4 m² area and adjusted for the seasonally dependent responsiveness to applied N. This work shows that yield potential prediction equations for winter wheat can be reliably established with only 2 years of field data. Furthermore, basing mid‐season N fertilizer rates on predicted yield potential and a response index can increase NUE by over 15% in winter wheat when compared to conventional methods. Using our optical sensor‐based algorithm that employs yield prediction and N responsiveness by location (0.4 m² resolution) can increase yields and decrease environmental contamination due to excessive N fertilization.     

CHANGES IN RESPONSE INDICES AS A FUNCTION OF TIME IN WINTER WHEAT

Nitrogen (N) responsiveness of crops can change with time as it is strongly influenced by in-season environmental conditions. This study was conducted to determine the relationship of N responsiveness using a response index (RI) as a function of time at five locations (Efaw, Stillwater, Lake Carl Blackwell, Perkins and Lahoma, Oklahoma) over a three-year period. Subplots of 4 m² were established at each experimental site that employed a randomized complete block design. Normalized Difference Vegetation Index (NDVI) readings were taken using a Greenseeker (NTech Industries, Inc., Ukiah, CA, USA) handheld sensor at various growth stages. The N responsiveness (RI_NDVI) was determined as the ratio of NDVI readings from a non-N limiting strip and the farmer practice. Then, RI was plotted against days where growing degree days (GDD = (T_min + T_max)/2—4.4°C) were > zero (DGDD > 0). At all sites, RI_NDVI increased with advancing stage of growth. Excluding Perkins 2005 and Stillwater 2006, the relationship between RI_NDVI and DGDD > 0 was positive and highly correlated. When the number of days from planting to sensing where DGDD > 0 was less than 60, it is unlikely that a reliable estimate of RI_NDVI could be obtained since values were all small (close to 1.0), consistent with limited growth at the early stages of growth. Averaged over years and sites for all growth stages, the correlation of RI_NDVI and RI_Harvest was positive and increased up to the Feekes 9 growth stage. Our results further suggested that once RI_NDVI is collected, it should be adjusted using the Equation RI_NDVIadj = RI_NDVI × [1.87/(DGDD > 0 ? 0.00997) + 0.5876].     

Furrow diking and N management for dryland wheat production in permanent raised beds

The permanent bed planting system for wheat (Triticum aestivum L.) production has recently received additional attention. Studies using hard red spring wheat (cultivar Nahuatl F2000) were conducted at two locations in central Mexico. The studies included the installation of three furrow diking treatments, two granular N timing treatments and three foliar N rates applied at the end of anthesis. The objective was to evaluate the effect of these factors on wheat grain yield, yield components and grain N in a wheat–maize (Zea maize L.) rotation with residues of both crops left as stubble. Results indicated that diking in alternate furrows increased both grain yield and the final number of spikes per m². The split application of N fertilizer enhanced the number of spikes per m² and grain N uptake, but the effect on grain yield was inconsistent. Similarly, grain protein increased with the foliar application of 6 kg N ha^?1, depending upon the maximum temperature within the 10 days following anthesis. The normalized difference vegetative index (NDVI) readings collected at four growth stages were generally higher for the split N application than for the basal N application at planting. Grain N uptake was associated to NDVI readings collected after anthesis.     

Utilizing Existing Sensor Technology to Predict Spring Wheat Grain Nitrogen Concentration

Optimum grain nitrogen (N) concentration and yield in spring wheat (Triticum aestivum L.) can be problematic without proper N fertilizer management. Sensor-based technologies have been used for application of fertilizers and also to predict yield in wheat, although little has been done in the prediction of grain N. Field studies were conducted in South Dakota in 2006 (Gettysburg, Bath, and Cresbard) and 2007 (Gettysburg, Aurora, Leola, and Artas). There were five N treatments (0, 56, 112, 168, and 224 kg N ha^?1) applied pre-plant with a second N application applied foliar at anthesis. Sensor readings were taken at growth stages Feekes 10, anthesis, and postfoliar application using the GreenSeeker Hand Held optical sensor. Grain samples were taken at maturity and analyzed for total N. Using similar information collected in 2003 and 2005, a critical normalized difference vegetation index (NDVI) value was determined using the Cate–Nelson procedure. The critical NDVI value needed to ensure optimum grain N was 0.70. In 2006 and 2007, the plots that received an application of N at anthesis had higher grain N than the plots not receiving N. There was also a significant response between applied N and grain yield. The results show that with further studies, the Greenseeker could be used to apply N to maximize yield and grain N in a precise and accurate manner.     

In-Season Prediction of Nitrogen Use Efficiency and Grain Protein in Winter Wheat (Triticum aestivum L.)

In a 3-year study, grain yield, nitrogen use efficiency (NUE), and grain protein (GP) were evaluated as a function of rate and timing of nitrogen (N) fertilizer application. Linear models that included preplant N, normalized difference vegetation index (NDVI), cumulative rainfall, and average air temperature from planting to sensing (T-avg) were evaluated to predict NUE and GP in winter wheat. GreenSeeker readings were collected at Feekes (F) 3, 4, 5, and 7 growth stages. Combined with rainfall and/or T-avg, NDVI alone was not correlated with NUE. However, NDVI and rainfall explained 45% (r² = 0.45) of the variability in GP at F7 growth stage. Preplant N, NDVI, rainfall and growing degree days (GDD) combined explained 76% (r² = 0.76) of the variability in GP at F3. Mid-season climatic data improved the prediction of GP and should therefore be considered for refining fertilizer recommendations when GP levels are expected to be low.     

OPTIMUM FIELD ELEMENT SIZE FOR MAXIMUM YIELDS IN WINTER WHEAT,USING VARIABLE NITROGEN RATES

The resolution at which variability in soil test and yield parameters exist is fundamental to the efficient use of real-time sensor-based variable rate technology. This study was conducted to determine the optimum field element size for maximum yields in winter wheat (Triticum aestivum L.), using variable nitrogen (N) rates based on sensor readings. The effect of applying N at four different resolutions (0.84, 3.34, 13.38, and 53.51 m²) on grain yield, N uptake and efficiency of use was investigated at Haskell, Hennessey, Perkins, and Tipton, Oklahoma. At Feekes growth stage 5 an optical sensor developed at Oklahoma State University measured red (670 ± 6 nm) and near-infrared (NIR, 780 ± 6 nm) reflectance in each subplot. A normalized-difference-vegetative-index (NDVI) was calculated from the sensor measurements. Nitrogen was applied based on a NDVI–N rate calibration. Nitrogen rate, yield, N uptake, and efficiency of use responses to treatment resolution and applied N fertilizer differed in the 3 years of this experiment. In the first year, no significant influence of resolution on N rate, yield, N uptake, or efficiency of use was observed, likely a result of a late freeze that drastically reduced yields. In the second year of the experiment, there was a trend for a lower N rate and a higher efficiency of use for the 0.84 m² resolution. In the third year of this study, there was a trend for a higher yield and a higher efficiency of use for the 53.51 m² resolution at both sites. In general, the finer resolutions tended to have increased efficiency of use in high yielding environments (>2300 kg ha^?1), and decreased yields in low yielding environments. This study indicates that application of prescribed fertilizer rates based on spatial variability at resolutions finer than 53.51 m² could lead to increased yields, decreased grower costs, and decreased environmental impact of excess fertilizers.     

On‐farm comparison of variable rates of nitrogen with uniform application to maize on canopy reflectance,soil nitrate,and grain yield

Recent development in canopy optical‐sensing technology provides the opportunity to apply fertilizer variably at the field scale according to spatial variation in plant growth. A field experiment was conducted in Ottawa, Canada, for two consecutive years to determine the effect of fertilizer nitrogen (N) input at variable‐ vs. uniform‐application strategies at the V6–V8 growth stage, on soil mineral N, canopy reflectance, and grain yield of maize (Zea mays L.). The variable N rates were calculated using an algorithm derived from readings of average normalized difference vegetation index (NDVI) of about 0.8 m × 4.6 m, and N fertilizer was then applied to individual patches of the same size of NDVI readings (0.8 m × 4.6 m) within a plot (2184 m²). Canopy reflectance, expressed as NDVI, was monitored with a hand‐held spectrometer, twice weekly before tasseling and once a week thereafter until physiological maturity. Soil mineral N (0–30 cm depth) was analyzed at the V6 and VT growth stages. Our data show that both variable and uniform‐application strategies for N side‐dressings based on canopy‐reflectance mapping data required less amount of N fertilizer (with an average rate of 80 kg N ha^–1 as side‐dressing in addition to 30 kg N ha^–1 applied at planting), and produced grain yields similar to and higher nitrogen‐use efficiency (NUE) than the preplant fully fertilized (180 kg N ha^–1) treatment. No difference was observed in either grain yield or NUE between the variable‐ and uniform‐application strategies. Compared to unfertilized or fully fertilized treatments, the enhancements in grain yield and NUE of the variable‐rate strategy originated from the later N input as side‐dressing rather than the variation in N rates. The variable‐rate strategy resulted in less spatial variations in soil mineral N at the VT growth stage and greater spatial variations in grain yield at harvest than the uniform‐rate strategy. Both variable‐ and uniform‐application strategies reduced spatial variations in soil mineral N at the VT stage and grain yield compared to the unfertilized treatment. The variable‐rate strategy resulted in more sampling points with high soil mineral N than the uniform‐rate strategy at the VT stage.     

Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Precise estimation of vegetable nitrogen (N) status is critical in optimizing N fertilization management. However, nondestructive and accurate N diagnostic methods for vegetables are relatively scarce. In our two-year field experiment, we evaluated whether an active canopy sensor (GreenSeeker) could be used to nondestructively predict N status of bok choy (Brassica rapa subsp. chinensis) compared with a chlorophyll meter. Results showed that the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) generated by the active canopy sensor were well correlated with the aboveground biomass (AGB) (r=0.698-0.967), plant N uptake (PNU) (r=0.642-0.951), and root to shoot ratio (RTS) (r=-0.426 to -0.845). Compared with the chlorophyll meter, the active canopy sensor displayed much higher accuracy (5.0%-177.4% higher) in predicting AGB and PNU and equal or slightly worse (0.54-1.82 times that of the chlorophyll meter) for RTS. The sensor-based NDVI model performed equally well in estimating AGB (R²=0.63) and PNU (R²=0.61), but the meter-based model predicted RTS better (R²=0.50). Inclusion of the days after transplanting (DAT) significantly improved the accuracy of sensor-based AGB (19.0%-56.7% higher) and PNU (24.6%-84.6% higher) estimation models. These findings suggest that the active canopy sensor has a great potential for nondestructively estimating N status of bok choy accurately and thus for better N recommendations, especially with inclusion of DAT, and could be applied to more vegetables with some verification.     

Use of spectral radiance for correcting nitrogen deficiencies and estimating soil test variability in an established bermudagrass pasture

The use of variable rate technology has become increasingly popular for applying plant nutrient elements. The most widely used method for determining variable fertilizer rates is presently based on soil testing and yield mapping. Three field studies (Bumeyville 1995, Burneyville 1996, and Ardmore 1996) were initiated in established Midland bermudagrass [Cynodon dacrylon (L) Pers.] pastures to determine the relationship between spectral radiance at specific wavelengths with forage nitrogen (N) removal and biomass, and to determine field variability of soil test parameters. Variable N (applied to 1.5 × 2.4 m subplots within 2.4 × 45.7 m main plots), fixed N and check treatments were evaluated at each location. Spectral radiance readings were taken in the red (671±6 nm), green (570±6 nm), and near infrared (NIR) (780±6 nm) wavelengths. The normalized difference vegetation index (NDVI) was calculated as NIR‐red/NIR+red. Variable N rates were applied based on NDVI. The highest fixed variable N rate was set at 224, 336, and 672 kg N ha^‐1 for Burneyville, 1995, 1996, and Ardmore, 1996, respectively. At Bumeyville, soil samples were collected in all variable rate plots (1.5 × 2.4 m) and analyzed for various soil test characteristics. NDVI, red, green, and NIR spectral radiance readings were correlated with bermudagrass forage N removal and yield. Correlation of forage yield and N removal with red, NIR, and NDVI were best with maximum forage production, however, when forage production levels were low correlation decreased dramatically for the red wavelength compared with NIR and NDVI. Forage yield and forage N removal in variable rate treatments increased when compared to the check while being equal to the half‐fixed and fixed rates where higher N rates were applied. Also, variability about the mean in variable rate plots was significantly lower than half‐fixed and fixed rates which supports adjusting N rates based on indirect NDVI measurements. Variable N rate plots reduced fertilizer inputs by 60% and produced the same yield as fixed rate plots, while fixed and half‐fixed rates did not increase N content in the forage over that of the variable rate treatment. Soil sample data collected from small consecutive plots (<4 m²) was extremely variable indicating that intense sampling would be needed if variable fertilizer application were to be based on soil test results.     

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer ...     

Use of nondestructive sensors to quantify ornamental kale nitrogen status

Greenhouse production of ornamental kale is popular for fall sales. The objective of this study was to evaluate if nondestructive handheld sensors could be used to quantify nitrogen (N) status in Brassica oleracea ‘Nagoya Red’. Topdressed fertilizer treatments of 0, 2.5, 5, 10, 15, and 20 g of 16-9-12 controlled release fertilizer (CRF) were utilized. Individual plants were scanned from 10 pots per treatment for Normalized Difference Vegetative Index (NDVI) values using a prototype NDVI pocket sensor, the recently released commercial GreenSeeker? handheld, and a Soil-Plant Analyses Development (SPAD) chlorophyll meter at three different rating dates starting 25 d after fertilizer treatment application (DAT). Linear and quadratic trends were observed within sampling dates. All three sensors showed correlations with leaf N depending on sampling time. Results indicated that 10 g CRF would be recommended for potted production.     

Effect of growth stage and variety on spectral radiance in winter wheat

Before sensor‐based variable rate technology (VRT) can be used to reduce nitrogen (N) fertilizer rates in winter wheat (Triticum aestivum L.) spectral radiance readings must be understood. One prominent issue is the impact of crop growth stage on spectral radiance readings, and the ensuing problem of relating databases gathered at different locations and different stages of growth. In order to evaluate the impact of growth stage on spectral radiance, sensor readings were taken from a winter wheat variety trial and two long‐term N and phosphorus (P) fertility trials. The normalized difference vegetative index was computed using red and near infrared (NIR) spectral radiance measurements [NDVI=(NIR‐red)/(NIR+red)]. TotalNuptake in winter wheat at Feekes growth stages 4, 5, 7, and 8 was highly correlated with NDVI. In the variety trial, non‐significant differences in ND VI readings were noticed between the five common genotypes (by growth stage) grown in this region. However, slopes from linear regression of total N uptake on NDVI were different at different stages of growth, which suggests the need for growth stage specific calibration. Freeze injury (altered tissue color) affected the relationship between total N uptake and NDVI, however, NDVI continued to be a good predictor of in‐season total N uptake in wheat even though cell blasting altered tissue color. This work showed that NDVI is a good predictor of biomass, but not necessarily total N concentration in plant tissue. The amount of variability in total N uptake as explained by NDVI increased with advancing growth stage (Feekes 4 to 7), largely due to an increased percentage of soil covered by vegetation.     

NITROGEN FERTILIZATION OPTIMIZATION ALGORITHM BASED ON IN-SEASON ESTIMATES OF YIELD AND PLANT NITROGEN UPTAKE

Current methods of determining nitrogen (N) fertilization rates in winter wheat (Triticum aestivum L.) are based on farmer projected yield goals and fixed N removal rates per unit of grain produced. This work reports on an alternative method of determining fertilizer N rates using estimates of early-season plant N uptake and potential yield determined from in-season spectral measurements collected between January and April. Reflectance measurements under daytime lighting in the red and near infrared regions of the spectra were used to compute the normalized difference vegetation index (NDVI). Using a modified daytime lighting reflectance sensor, early-season plant N uptake between Feekes physiological growth stages 4 (leaf sheaths lengthen) through 6 (first node of stem visible) was found to be highly correlated with NDVI. Further analyses showed that dividing the NDVI sensor measurements between Feekes growth stages 4 and 6, by the days from planting to sensing date was highly correlated with final grain yield. This in-season estimate of yield (INSEY) was subsequently used to compute the potential N that could be removed in the grain. In-season N fertilization needs were then considered to be equal to the amount of predicted grain N uptake (potential yield times grain N) minus predicted early-season plant N uptake (at the time of sensing), divided by an efficiency factor of 0.70. This method of determining in-season fertilizer need has been shown to decrease large area N rates while also increasing wheat grain yields when each 1m² area was sensed and treated independently.     

Dedicated to the 100th birthday of Professor Dr. Dr. h. c. mult. Eduard von Boguslawski

Identifying the optimum resolution where differences in corn (Zea mays L.) grain yields are detectable could theoretically improve nitrogen (N) management, thereby resulting in economic and environmental benefits for producers and the public at large. The objective of this study was to determine the optimum resolution for prediction of corn grain yield using indirect sensor measurements. Corn rows, 15–30 m long, were randomly selected at three locations where the exact location of each plant was determined. In 2005 and 2006, four of eight rows at each location were fertilized with 150 kg N ha^?1 as urea ammonium nitrate (28% N). A GreenSeeker? optical sensor was used to determine average Normalized Difference Vegetation Index (NDVI) across a range of plants and over fixed distances (20, 40, 45.7, 60, 80, 91.4, 100, 120, 140, 160, 180, 200, 220, and 240 cm). Individual corn plants were harvested and grain yield was determined. Correlation of corn grain yield versus NDVI was evaluated over both increasing distances and increasing number of corn plants. Then, the squared correlation coefficients (r_cc ²) from each plot (used as data) were fitted to a linear plateau model for each resolution treatment (fixed distance and number of corn plants). The linear-plateau model coefficient of determination (r_lp ²) was maximized when averaged over every four plants in 2004 and 2006, and over 11 plants in 2005. Likewise, r_lp ² was maximized at a fixed distance of 95, 141, and 87 cm in 2004, 2005, and 2006, respectively. Averaged over sites and years, results from this study suggest that in order to treat spatial variability at the correct scale, the linear fixed distances should likely be <87 cm or <4 plants as an optimum resolution for detecting early-season differences in yield potential and making management decisions based on this resolution.     

Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data

A simplified evaporative fraction (Λ) based single-source energy balance scheme was tested with moderate resolution (1 km) noontime satellite observations to evaluate clear sky latent heat flux (λE) estimates over diverse agricultural landscapes. This approach uses two-dimensional (2D) scatter between land surface temperature (LST) and albedo to determine Λ. The operational utility of this scheme was demonstrated for estimating regional evapotranspiration and consumptive water use during rabi (November to April) crop growing season to predict pre-harvest wheat yield (error within 15.9% of reported mean) using time series data. The existence of triangular relations between Λ and LAI (leaf area index) or NDVI (normalized difference vegetation index) was found with basal line (hypotenuse) linearly coupled with LAI or NDVI at low level of surface soil wetness. The analysis of diurnal course of in situ Λ proved the validity of constant-Λ hypothesis over pure, uniform, homogeneous crop canopies but showed irregular and wave-like patterns over heterogeneous, mixed crop canopies. The root mean square error (RMSE) of noontime and daytime average λE estimates with respect to in situ λE measurements were also smaller over homogeneous agricultural canopies (41 and 23 W m⁻²) with correlation coefficients (r) 0.94 and 0.96, respectively, from 135 clear sky datasets as compared to RMSE over heterogeneous ones (59 and 28 W m⁻² with r = 0.66 and 0.82, respectively from 22 datasets). The intercomparison with another Λ based approach (LST–NDVI 2D scatter) showed the supremacy of Λ determined from LST–albedo 2D scatter. The efficiency of LST–NDVI scatter was better during the dry down or water limited phases of crop growth only. The uncertainties of λE estimates were attributed to errors in core radiation budget inputs, relative loss of conservativeness of Λ due to canopy heterogeneity, and the inherent limitations of the single-source approach. There is further scope to reduce present λE uncertainties by combining the new findings on Λ (LST–albedo scatter)–NDVI triangular relations, diurnal Λ and two-source radiation budget.     

Subcritical soil hydrophobicity in the presence of native and exotic arbuscular mycorrhizal species at different soil salinity levels

Soil salinity and arbuscular mycorrhizal fungi (AMF) influence the soil hydrophobicity. An experiment was performed to determine the effects of soil salinity and AMF species on soil water repellency (SWR) under wheat (Triticum aestivum L.) crop. Six AMF treatments, including four exotic species (Rhizophagus irregularis, Funneliformis mosseae and Claroideoglomus claroideum, a mix of three species), one mix native AMF species treatment and an AMF-free soil in combination with four salinity levels (1, 5, 10, and 15 dS m^?1) were used. The soil repellency index (RI) increased with salinity increment ranging from 2.4 to 10.5. The mix of three exotic and native AMF treatments enhanced the RI significantly compared to AMF-free soil in all salinity levels with one exception for native treatment at 1 dS m^?1. Among individual AMF species, the C. claroideum treatment at 10 dS m^?1 increased the RI by 67% compared to AMF-free soil. The native AMF treatment was more efficient in root colonization, glomalin production and SWR development at 10 and 15 dS m^?1, compared to exotic species. In addition to the net positive effect of salinity on SWR, the AMF influences on the RI were greatly dependent on salinity levels.