Normalized Difference Vegetation Index Readings Research Articles

Predicting chlorophyll and nitrogen content in rice using multiple regression models

Chlorophyll and nitrogen (N) content in rice at critical crop growth stages are important input parameters for crop growth and nutrient uptake models used to optimize N application. Hence, the study was conducted to predict chlorophyll and N content in rice using multiple regression-based models involving normalized difference vegetation index (NDVI), Soil Plant Analysis Development (SPAD), Customized leaf color chart (CLCC), chlorophyll and N content. An experiment was conducted with six varieties i.e. CR Dhan 312, CR Dhan 310, Lalat, Shatabdi, Swarna Shreya, CR Dhan 206 with four N levels (i.e. 0, 80, 100 and 120 kg N ha−1) in split plot design and replicated thrice. The NDVI, SPAD and CLCC readings were taken at 45, 49 and 54 days after transplanting (DAT). Plant sampling was done for estimating the chlorophyll and nitrogen content in laboratory following standard methodology. Multiple linear regressions were performed between NDVI, SPAD and CLCC readings and chlorophyll & N content in rice in different variables combinations. Multiple linear regression (MLR) models with two variables i.e. NDVI and SPAD predicted the chlorophyll and N content of rice varieties. The predicted values for nitrogen and chlorophyll were comparable to those obtained using models from individual varieties when combined data from six different varieties were used to create MLR models. The study concludes that variations in chlorophyll and N concentrations in rice can be estimated using data from optical sensors with notable accuracies.

Optical sensor-based nitrogen management: an environmentally friendly and cost-effective approach for sustainable wheat (Triticum aestivum L.) production on Eastern plains of India

An optical sensor like Green Seeker (GS) is an emerging tool for site-specific in-season fertilizer nitrogen management strategy. The objective of this study was to establish an in-season estimate of yield (INSEY)–grain yield (GY) relation in wheat grown under Eastern plains of India using normalized difference vegetation index (NDVI) at 45 and 65 days after sowing (DAS). Data revealed lower NDVI values at 65 DAS over 45 DAS in no-nitrogen (N), phosphorus (P), and potassium (K) applied control plots as well as in N-rich plots (225 kg ha−1 N); on the contrary, the values were higher at 65 DAS over 45 DAS in treatments where some N fertilizers were added based on NDVI readings at 45 DAS. Response index (RI) showed higher chances of response to external application of N in NDVI-based treatments. The INSEY–GY relation for wheat at 45 and 65 DAS was worked out as a power function of y = 64265x1.171 and y = 46949x1.036 (y is the attainable yield in kg ha−1 and x is INSEY), respectively. The yields could fairly be predicted through this relation even at 45 DAS, though the relationship was more robust at 65 DAS (R2 = 0.94). A prescriptive dose of 60 kg N ha−1 as basal + 60 kg N ha−1 at crown root initiation (CRI) stage followed by NDVI sensor-guided N application (at 45 and 65 DAS) brought about a significant improvement in yield performances, N use efficiencies with higher net returns, and benefit-to-cost ratio. The results proved the reliability of the NDVI sensor as an important tool for the optimization of fertilizer nitrogen in wheat grown under the Eastern plains of India. The new INSEY–GY relation developed through this trial could successfully be used for yield prediction in the Eastern plains of India under changing climate.

Evaluating the impact of flood inundation with the cloud computing platform over vegetation cover of Ganga Basin during COVID-19

Around the month of July 2020, the people suffering from the COVID-19 pandemic got added burden of disastrous floods. The current study tries to understand the impact of flood inundation on the vegetation cover across the Ganga Basin with the help of a cloud-based computing platform like Google Earth Engine (GEE) in a near real-time scenario. This offers a semi-automatic scheme with customized algorithms to process the large scale of datasets to further analyze and derive the required information from space-based satellite images like Synthetic Aperture Radar (SAR) images. The direct impact of floods on vegetation cover is assessed with the satellite-derived normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results of post-flood NDVI and EVI readings showed a considerable reduction due to the protracted flood water submergence. The results show the flood extent maps for the selected regions for the lower Ganga Basin having inundations in parts of Bihar and West Bengal. This affects the settlements, agricultural land, and vegetation cover with damage to the large population. The outcomes of the work may be utilized to assess the precise flood-inundated areas at a faster rate to offer a faster response even in the pandemic.

Prediction of grain protein content and gluten quality of bread wheat in the early vegetation period by optical sensors

Estimation of the end-use quality characteristics of bread wheat in the early phenological period and before harvest is critically important for the flour industry. This study investigated the usability of the Normalized Difference Vegetation Index (NDVI) and Chlorophyll Meter (CM) values obtained by GreenSeeker and Chlorophyll Meter for prediction of protein content and gluten quality in the early growth stage of bread wheat. The results demonstrated that protein content was positively correlated with NDVI and CM values of the optical sensors, which can be predicted by using the equation developed with NDVI Zadoks (ZD)3.1 period values (R2 = 0.92, p <0.01). Compared to the NDVI values, higher correlated equations were obtained with the CM for the sedimentation volumes. Both sensor readings showed strong correlation with the GlutoPeak GSI (Gluten Strength Index) and GRI (Gluten Rheological Index) values in the ZD3.1 period. Also, while Cu, Fe and Mn contents of the grain tended to increase with increasing nitrogen doses according to both optical sensor readings, Zn content was inversely proportional to the NDVI and CM readings. The results of this study reveal that the grain protein content and gluten quality of bread wheat can be estimated successfully in the ZD3.1 period.

Evaluation of peanut tolerance to mid-season applications of PPO-Inhibitor herbicides mixed with different surfactants

Protoporphyrinogen oxidase (PPO) inhibitor herbicides are being increasingly used to control acetolactate synthases (ALS) inhibitor-resistant weeds in peanuts (Arachis hypogaea L.). However, PPO-inhibitor herbicides can injure the crop under certain application conditions, especially under abiotic stress and surfactants may exacerbate this injury. The objectives of this study were to 1) investigate the effect of PPO-inhibitor based treatments on dryland peanut growth and yield when applied at three timings in mid-season, 2) evaluate the interactions of surfactants, chloroacetamide herbicides, and PPO-inhibitors, and 3) assess the level of correlation of normalized difference vegetation index (NDVI) readings to traditional visible injury rating. Field studies were conducted in Henry and Escambia counties in Alabama, U.S. during 2018, and 2019. Up to 55% of visible peanut injury was observed with acifluorfen, lactofen, and carfentrazone-ethyl treatments. In general, the NDVI readings correlated significantly with traditional visible injury ratings. A tank mixture of chloroacetamide herbicides (pyroxasulfone, S-metolachlor, dimethenamid-P) with lactofen did not lead to more injury or yield loss than lactofen applied alone. Yield losses up 27% were observed with carfentrazone-ethyl plus a high surfactant oil concentrate (HSOC) at 75 and 90 days after planting (DAP) as compared to the non-treated check (NTC). Overall, treatments with HSOC and/or carfentrazone-ethyl were more likely to cause significant injury and yield loss than treatments with acifluorfen or lactofen plus nonionic surfactant (NIS). Peanuts are more sensitive to PPO-inhibitor herbicides at 75 DAP. NDVI did provide additional plant health information to subjective injury ratings, however, neither of these measurements are reliable predictors of peanut yield loss.

Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings&amp;mdash;providing a tool for tracking and monitoring nutrient management effects&amp;mdash;not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI &lt; 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

Applied use of growing degree days to refine optimum times for nitrogen stress sensing in winter wheat

AbstractVariable influence of the environment on early‐season plant growth leads to similarly variable yield levels from year to year. This study was conducted to determine the ideal point in the growing season when normalized difference vegetation index (NDVI) sensor readings were highly correlated with grain yield. For each site‐year, NDVI readings were collected at least seven times from December through April. Readings were collected from two long‐term experiments where an N response was expected in plots that historically received different N rates. The number of days from planting to sensing where growing degree days (GDD) were more than 0 (GDD &gt; 0) was tabulated by site‐year for all dates when NDVI data were collected. The r2 was computed for NDVI versus final grain yield at all sensing dates and plotted against the respective GDD &gt; 0 when readings were taken. Linear plateau models were used to determine the point when the r2 peaked. Averaged over 3 yr (2016–2018), the optimum GDD &gt; 0 needed to predict grain yield using NDVI in both long‐term trials was between 97 and 112. Use of the GDD &gt; 0 as a numeric metric to delineate the best time and date to collect NDVI readings and predict yield potential can then be used to formulate accurate midseason fertilizer N rates. Adhering to quantitative GDD &gt; 0 data is much more reliable than using subjective morphological scales. These critical GDD values can be reported on a day‐to‐day, by‐location basis (mesonet.org) for in‐season producer use.

Value of composite Normalized Difference Vegetative Index and growing degree days data in Oklahoma, 1999 to 2018

AbstractFor over 25 yr, sensor‐based Normalized Difference Vegetative Index (NDVI) data has been collected from both satellite imagery and near‐plant (3‐m) readings. Because calibrated NDVI data coming from active sensors is still relatively new, limited research has returned to evaluate databases including multiple years and environments. Composite NDVI readings and final grain yield were collected from 1999 to 2018. This included growing degree day (GDD) records for each mid‐season sensor measurement. This was attempted to potentially improve the use of a historical and subjective morphological scale. Using location‐specific‐archived‐data from the Oklahoma Mesonet, the exact number of days from planting to sensing where GDD &gt; 0 for each date and location were compiled. The ensuing relationship between NDVI (for a predetermined GDD &gt; 0 range) and yield was determined. Grain yield prediction was improved between 80 and 115 GDDs. These ranges further targeted a climatologically identifiable metric that precisely determined when to collect sensor readings in future years. Compared with the current composite yield prediction equation for Oklahoma, the new exponential function created from this study was higher in the lower‐yielding environments. Underestimation of fertilizer N rates has been voiced by producers in recent years. This has likely been the product of more current varieties, more efficient farming practices, and increased optimum N rates needed for higher yields. This validates the adoption of a new YP0 equation for OSU's on‐line Sensor Based Nitrogen Rate Calculator, allowing accurate yield prediction between 80 and 115 GDDs.

EFFECT OF HEIGHT, TILT AND TWIST ANGLES OF AN ACTIVE REFLECTANCE SENSOR ON NDVI MEASUREMENTS

The application of nitrogen (N) fertilizer is complex and expensive, so its correct management has financial and environmental benefits. The use of optical proximity sensors is a promising technique. However, the movement of the agricultural machinery or of the person carrying the sensor will result in height differences and/or different tilt and twist angles with respect to the canopy. We considered whether these variations would affect the reflectance measurement. In this study, we took normalized difference vegetation index (NDVI) readings of a wheat canopy, to which 90 kg ha-1 of urea had been applied in stage 5, and observed the NDVI in stages 6, 8 and 10.5. We also tested soybeans, to which 90 kg ha-1 of urea had been applied in stage R1, and took NDVI readings in stages R2 and R5. Our goal was to study the effects of the position of an active reflectance sensor (GreenSeeker) on the NDVI index at different heights and at different angles to the canopy. We observed that the height of the sensor affected the NDVI depending on the stage of the plant and that angles up to 15° of the sensor did not directly affect the readings.

Comparison of Trimble GreenSeeker and Crop Circle (Model ACS-210) Reflectance Meters for Assessment of Severity of Late Leaf Spot

ABSTRACT Four field experiments conducted in 2015 were used to examine the relationships among normalized difference vegetation index (NDVI) measurements from two canopy crop sensors and visual estimates of defoliation by late leaf spot (Nothopassalora personata) of peanut (Arachis hypogaea) the predominant foliar disease in this study. For each evaluation, reflectance was measured with each the two meters, and leaf spot severity was measured visually within as short a time as possible. Linear and quadratic regression models were used to characterize the relationships between percent defoliation from late leaf spot and NDVI measured with the GreenSeeker (NDVIGS) and Crop Circle (NDVICC) instruments and the relationships between NDVIGS and NDVICC. NDVIGS decreased with increasing percent defoliation according to linear or quadratic functions in three of the four trials, NDVICC decreased with increasing percent defoliation according to linear functions in three of four trials. In two of the four trials, NDVICC increased with increasing NDVIGS according to quadratic functions, but there was no significant regression for those variables in two trials. In three of the four trials, NDVICC linear regression had a better fit for predicting percent defoliation according to the coefficient of determination (R2). There was no indication for either instrument that the same NDVI reading corresponded with the same level of defoliation across trials. Results indicated that NDVI measurements from the two instruments are not interchangeable.

Precision Agriculture Application for Sustainable Nitrogen Management of Justicia brandegeana Using Optical Sensor Technology

Over-fertilization is a common practice in ornamental nursery production. Oftentimes, visual analysis is used to determine plant nutrient levels, leading to less accurate estimates of fertilizer application. This study focused on exploring the suitability of two non-destructive sensors, Soil Plant Analysis Development (SPAD-502) and GreenSeekerTM, for measuring plant tissue nutrient uptake. Florikan Top-Dress fertilizer 12N-6P-8K was applied to Justicia brandegeana in various increments (0, 10, 20, 30, 40, and 50 g) to simulate plants with deficient to excessive nitrogen rates. Various parameters were recorded including Normalized Difference Vegetation Index (NDVI) and SPAD readings, soil leachate analysis (nitrates and phosphate), and total leaf carbon:nitrogen (C:N). The NDVI and SPAD readings were recorded biweekly for three months after the initial controlled release fertilizer (CRF) treatments. Leaf C:N was analyzed through dry combustion while nitrates and phosphate were determined from soil leachate. Results suggest that the smaller amount (20 g) of CRF is as effective in providing N to J. brandegeana as larger amounts (30, 40, 50 g). Implementation of this fertilizer regimen will result in reduced agricultural nutrient runoff and overall negative environmental impacts. Application of optical sensor technology using SPAD and GreenSeekerTM showed promising results in determining the fertilizer requirements of J. brandegeana. This method could serve as a guideline for nursery producers and landscape personnel as a fast and non-destructive tool for sustainable fertilizer management practices within the ornamental plant industry.

Normalized difference vegetation index, and K+ in stem sap of potato plants (Group Andigenum) as affected by fertilization

AbstractRemote sensors permit forecasting the nutrient status and yields in crops of economic importance in Colombia. The objective of this study was to determine the relationships between normalized difference vegetation index (NDVI) and yield as well as concentrations of ${\rm {N - NO}}_3^ - $ and K+ in stem sap of potato cultivars Diacol Capiro and Pastusa Suprema (Solanum tuberosum L., Group Andigenum) in relation to different fertilizer rates. Increasing doses (0, 1450, 1900 and 2375 kg ha–1) of macro- and micronutrient fertilizers were applied to determine NDVI behavior at 55, 75, 100, 125 and 150 days after planting. For Capiro, significant differences in NDVI readings (0.84–0.88) were found between phenological stages. In both cultivars, NDVI correlated positively with yield and K+ concentrations in stem sap during tuber filling and maturation, while in Capiro no correlation was established between NDVI values and ${\rm {N - NO}}_3^ - $ concentrations in stem sap. The NDVI readings could be used to forecast productivity and K status in potato Group Andigenum.

ASSESSING NITROGEN NUTRITIONAL STATUS, BIOMASS AND YIELD OF COTTON WITH NDVI, SPAD AND PETIOLE SAP NITRATE CONCENTRATION

SUMMARYCanopy normalized difference vegetation index (NDVI), soil plant analysis development (SPAD) reading and petiole sap NO3−‒N concentration are increasingly used as quick and non-destructive methods to monitor plant N nutrition and growth status and predict yield of crops. However, little information is available on the comparisons of these three methods in assessing N nutrition, growth and yield for cotton (Gossypium hirsutum L.). Four N rates (0, 34, 67 and 101 kg N ha−1) under two cover conditions [no cover crop and hairy vetch (Vicia villosa) crop] in a 33-year long-term field trial were used to evaluate how canopy NDVI, SPAD reading (related to chlorophyll content) and petiole sap NO3−‒N concentration (conventional method) are able to assess N nutrition and plant biomass and predict yield for cotton. Canopy NDVI and SPAD readings responded less sensitively to N rates than petiole sap NO3−‒N. The responses of NDVI and SPAD reading to N rates were generally reduced due to the winter cover crop with hairy vetch. Significant and positive correlations existed mostly among NDVI, SPAD reading, and petiole sap NO3−‒N concentration. Canopy NDVI during mid-bloom to late bloom and SPAD reading during early bloom to late bloom were effective alternative methods for assessing cotton N nutrition status. The SPAD reading at late bloom was an effective parameter to estimate cotton biomass. The NDVI at early square and SPAD reading during early square to mid-bloom were effective for cotton yield prediction.

A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine.

In Maine, potato yield is consistent, 38 t·ha−1, for last 10 years except 2016 (44 t·ha−1) which confirms that increasing the yield and quality of potatoes with current fertilization practices is difficult; hence, new or improvised agronomic methods are needed to meet with producers and industry requirements. Normalized difference vegetative index (NDVI) sensors have shown promise in regulating N as an in season application; however, using late N may stretch out the maturation stage. The purpose of the research was to test Trimble GreenSeeker® (TGS) and Holland Scientific Crop Circle™ ACS-430 (HCCACS-430) wavebands to predict potato yield, before the second hilling (6–8 leaf stage). Ammonium sulfate, S containing N fertilizer, is not advised to be applied on acidic soils but accounts for 60–70% fertilizer in Maine’s acidic soils; therefore, sensors are used on sulfur deficient site to produce sensor-bound S application guidelines before recommending non-S-bearing N sources. Two study sites investigated for this research include an S deficient site and a regular spot with two kinds of soils. Six N treatments, with both calcium ammonium nitrate and ammonium nitrate, under a randomized complete block design with four replications, were applied at planting. NDVI readings from both sensors were obtained at V8 leaf stages (8 leaf per plant) before the second hilling. Both sensors predict N and S deficiencies with a strong interaction with an average coefficient of correlation (r2) ~45. However, HCCACS-430 was observed to be more virtuous than TGS. The correlation between NDVI (from both sensors) and the potato yield improved using proprietor-proxy leaf area index (PPLAI) from HCCACS-430, e.g., r2 value of TGS at Easton site improve from 48 to 60. Weather data affected marketable potato yield (MPY) significantly from south to north in Maine, especially precipitation variations that could be employed in the N recommendations at planting and in season application. This case study addresses a substantial need to revise potato N recommendations at planting and develop possible in season N recommendation using ground based active optical (GBAO) sensors.

Development of an NDVI‐Based Nitrogen Rate Calculator for Cotton

The use and adoption of optical sensors to determine midseason nitrogen (N) application rates in cereal grain production has gained increasing acceptance by producers in the past decade; however, the technology has yet to impact mainstream cotton (Gossypium hirsutum L.) production. Overapplication of N in cotton leads to excessive growth and can result in a dramatic yield decrease. This study was designed to develop a sensor‐based cotton yield prediction model using normalized difference vegetation index (NDVI) readings from an optical sensor, incorporate the model into an algorithm used to determine midseason N rates, and determine if N response can be predicted using NDVI values collected midseason. A GreenSeeker hand held sensor was used to measure NDVI from seven studies at Lake Carl Blackwell (LCB), Stillwater, OK, and Southwest Research Station (SWR), Altus, OK, from 2006 to 2010. Normalized difference vegetation index readings taken at white flower stage had a good correlation with final yield. Final yield prediction was improved when NDVI was normalized using cumulative growing degree day (CumGDD) measured between planting and sensing. Sorting NDVI by CumGDD to 940 to 1200 and 1400 to 1500 at LCB and SWR sites, respectively, extended the critical sensing window from match‐head square to white flower stages. This study showed that yield potential in cotton could be predicted within the season using NDVI, which confirms the potential for using sensor‐based N rate recommendations in cotton.

NDVI variation according to the time of measurement, sampling size, positioning of sensor and water regime in different soybean cultivars

Although the information on the Normalized Difference Vegetation Index (NDVI) in plants under water deficit is often obtained from sensors attached to satellites, the increasing data acquisition with portable sensors has wide applicability in agricultural production because it is a fast, nondestructive method, and is less prone to interference problems. Thus, we carried out a set of experiments to investigate the influence of time, spatial plant arrangements, sampling size, height of the sensor and water regimes on NDVI readings in different soybean cultivars in greenhouse and field trials during the crop seasons 2011/12, 2012/13 and 2013/14. In experiments where plants were always evaluated under well-watered conditions, we observed that 9 a.m. was the most suitable time for NDVI readings regardless of the soybean cultivar, spatial arrangement or environment. Furthermore, there was no difference among NDVI readings in relation to the sampling size, regardless of the date or cultivar. We also observed that NDVI tended to decrease according to the higher height of the sensor in relation to the canopy top, with higher values tending to be at 0.8 m, but with no significant difference relative to 1.0 m—the height we adopted in our experiments. When different water regimes were induced under field conditions, NDVI readings measured at 9 a.m. by using a portable sensor were successful to differentiate soybean cultivars with contrasting responses to drought.

Can Frequent Measurement of Normalized Difference Vegetative Index and Soil Nitrate Guide Nitrogen Fertilization of Kentucky Bluegrass Sod?

Objective approaches to guide N fertilization of turfgrass sod crops are lacking. This study was conducted to determine the relationships among normalized difference vegetative index (NDVI), frequently‐measured soil NO3–N concentrations, and peak‐shear force of predominately Kentucky bluegrass (Poa pratensis L.) sod, and to evaluate if those relationships could help guide N fertilization. Ramp calibration strips (RCS) with varying N rates were established within production sod fields in Rhode Island across three consecutive years. At 2‐wk intervals during each growing season, soil NO3–N concentrations and NDVI readings were recorded and correlated. Biweekly relative NDVI readings plateaued when soil NO3–N concentrations ranged between 5 and 12 mg kg–1. Mean relative NDVI plateaued at 196 kg N ha–1 yr–1. Relative sod peak‐shear force was negatively correlated to soil NO3–N concentrations and the total amount of N applied per year. Relative peak‐shear force was maximized when mean relative NDVI readings ranged from 0.969 to 0.982, but declined as mean relative NDVI increased towards 1.0. The results suggest that frequently measured NDVI and soil NO3–N concentrations show promise as guides for N fertilization of predominately Kentucky bluegrass sod. Further research will be required to validate these approaches on larger‐scale sod fields.

Use of optical sensor for in-season nitrogen management and grain yield prediction in maize

Precision agriculture technologies have developed optical sensors which can determine plant’s normalized difference vegetation index (NDVI).To evaluate the relationship between maize grain yield and early season NDVI readings, an experiment was conducted at farm land of National Maize Research Program, Rampur, Chitwan during winter season of 2012. Eight different levels of N 0, 30, 60, 90, 120, 150, 180 and 210 kg N/ha were applied for hybrid maize RML 32 × RML 17 to study grain yield response and NDVI measurement. Periodic NDVI was measured at 10 days interval from 55 days after sowing (DAS) to 115 DAS by using Green seeker hand held crop sensor. Periodic NDVI measurement taken at a range of growing degree days (GDD) was critical for predicting grain yield potential. Poor exponential relationship existed between NDVI from early reading measured before 208 GDD (55 DAS) and grain yield. At the 261GDD (65DAS) a strong relationship (R2 = 0.70) was achieved between NDVI and grain yield. Later sensor measurements after 571 GDD (95DAS) failed to distinguish variation in green biomass as a result of canopy closure. N level had significantly influenced on NDVI reading, measured grain yield, calculated in season estimated yield (INSEY), predicted yield with added N (YPN), response index (RI) and grain N demand. Measuring NDVI reading by GDD (261–571 GDD) allow a practical window of opportunity for side dress N applications. This study showed that yield potential in maize could be accurately predicted in season with NDVI measured with the Green Seeker crop sensor.Journal of Maize Research and Development (2015) 1(1):64-70DOI: http://dx.doi.org/10.5281/zenodo.34261

Influence of soil, crop residue, and sensor orientations on NDVI readings

Site-specific in-season corn (Zea mays L.) nitrogen (N) rate recommendations based on remote sensing can increase nitrogen use efficiency (NUE) but most approaches require the corn to be at the V8 growth stage. Success in estimating N needs during early vegetative growth has been limited due to low plant biomass and background interference. The objective of this experiment was to measure the influence of soil series, soil moisture, surface crop residues, and sensor orientation on Normalized Difference Vegetation Index (NDVI) from soils prior to planting and corn from planting through the V6 growth stage. A controlled experiment was conducted in Virginia using four soil types commonly used for corn production. Spectral reflectance readings from the soils and four crop residues were measured using the GreenSeeker® sensor at four sensor orientations. Sensor orientation affected NDVI readings from bare soils before planting, with a difference of up to 0.16 units among orientations. The 15 cm mask generally resulted in the lowest NDVI. Wetting soils resulted in greater NDVI values from all soils with differences of up to 0.18 units between the same soil wet and dry. Values for NDVI were initially influenced by crop residue type but no differences due to type were detected once corn reached the V4 stage. Altering sensor orientation generally changed NDVI values but none resulted in NDVI that was similar across all soil and residue backgrounds. Background (soil and residue) influenced NDVI readings during early vegetative corn growth so the sensing background for the calibration reference areas should be uniform and similar to the larger field for implementation of the GreenSeeker® variable rate system.