Reflectance Measurements from Aerial and Proximal Sensors Provide Similar Precision in Predicting the Rice Yield Response to Mid-Season N Applications.

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha-1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency.

Field evaluation of slow-release nitrogen fertilizers and real-time nitrogen management tools to improve grain yield and nitrogen use efficiency of spring maize in Nepal.

Several innovative fertilizers and application methods, along with different decision support tools have been developed to improve nitrogen use efficiency (NUE) and crop yields, but their comparative study in maize is yet to be done in Nepal. Thus, we evaluated different slow-release N fertilizers and decision-making tools for real-time N management compared with the common urea on their effectiveness in increasing NUE, grain yield and economic return of spring maize (Zea mays L. cv. Rampur Hybrid-10). A field trial was conducted at Dang Valley of Nepal in a Randomized complete block design with three replications and seven treatments; N omission- (0 kg N ha-1), normal urea at 120 kg N ha-1 (recommended dose, N120), and 180 kg N ha-1(N180), Polymer Coated Urea (PCU- 90 kg N ha-1), Urea Briquette-deep placement (UDP- 90 kg N ha-1), GreenSeeker (GS- 143 kg N ha-1) and Leaf Color Chart based N management (LCC- 143 kg N ha-1). N application based on decision support tools (LCC and GS) and innovative fertilizers (UDP, PCU) yielded 17.35-45.81% more grain yield than recommended dose (RDF). The real time nitrogen application through LCC and GreenSeeker and slow release N fertilizer (PCU and UDP) resulted in higher agronomic efficiency of nitrogen- AEN (21.30-27.82 kg grain kg-1 N) compared to RDF (12.15 kg grain kg-1 N) and N180 (19.87 kg grain kg-1 N). UDP, with 25% less N compared to RDF, resulted in higher grain yield (5.25 t ha-1), partial factor productivity of N- PFPN (58.37 kg grain kg-1 N) and AEN (27.82 kg grain kg-1 N). Based on the economic return and ease in the application, both UDP and LCC based N application seem promising in Nepalese conditions. However, their effectiveness should be validated across diverse agro-ecologies, soil types and climatic conditions for a general recommendation.

Influence of light intensity distribution characteristics of light source on measurement results of canopy reflectance spectrometers.

BACKGROUND: The characteristics of light source have an important influence on the measurement performance of canopy reflectance spectrometer. The size of the effective irradiation area and the uniformity of the light intensity distribution in the irradiation area determine the ability of the spectrometer to express the group characteristics of the measured objects. METHODS: In this paper, an evaluation method was proposed to theoretically analyze the influence of the light intensity distribution characteristics of the light source irradiation area on the measurement results. The light intensity distribution feature vector and the reflectance feature vector of the measured object were constructed to design reflectance difference coefficient, which could effectively evaluate the measurement performance of the canopy reflectance spectrometer. By using self-design light intensity distribution test system and GreenSeeker RT100, the evaluation method was applied to evaluate the measurement results. RESULTS: The evaluation results showed that the vegetation indices based on the arithmetic average reflectance of the measured object could be obtained theoretically only when the light intensity distribution of the light source detected by the spectrometer was uniform, which could fully express the group characteristics of the object. When the light intensity distribution of the active light source was not uniform, the measure value was difficult to fully express the group characteristics of the object. And the measured object reflectance was merely the weighted average value based on the light intensity distribution characteristics. CONCLUSIONS: According to the research results of this paper, sunlight is the most ideal detection light source. If the passive light source spectrometer can improve the measurement method to adapt to the change of sunlight intensity, its measurement performance will be better than any active-light spectrometer.

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries.

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

Evaluating RGB Imaging and Multispectral Active and Hyperspectral Passive Sensing for Assessing Early Plant Vigor in Winter Wheat.

Plant vigor is an important trait of field crops at early growth stages, influencing weed suppression, nutrient and water use efficiency and plant growth. High-throughput techniques for its evaluation are required and are promising for nutrient management in early growth stages and for detecting promising breeding material in plant phenotyping. However, spectral sensing for assessing early plant vigor in crops is limited by the strong soil background reflection. Digital imaging may provide a low-cost, easy-to-use alternative. Therefore, image segmentation for retrieving canopy cover was applied in a trial with three cultivars of winter wheat (Triticum aestivum L.) grown under two nitrogen regimes and in three sowing densities during four early plant growth stages (Zadok's stages 14⁻32) in 2017. Imaging-based canopy cover was tested in correlation analysis for estimating dry weight, nitrogen uptake and nitrogen content. An active Greenseeker sensor and various established and newly developed vegetation indices and spectral unmixing from a passive hyperspectral spectrometer were used as alternative approaches and additionally tested for retrieving canopy cover. Before tillering (until Zadok's stage 20), correlation coefficients for dry weight and nitrogen uptake with canopy cover strongly exceeded all other methods and remained on higher levels (R² > 0.60***) than from the Greenseeker measurements until tillering. From early tillering on, red edge based indices such as the NDRE and a newly extracted normalized difference index (736 nm; ~794 nm) were identified as best spectral methods for both traits whereas the Greenseeker and spectral unmixing correlated best with canopy cover. RGB-segmentation could be used as simple low-cost approach for very early growth stages until early tillering whereas the application of multispectral sensors should consider red edge bands for subsequent stages.

Active optical sensor assessment of spider mite damage on greenhouse beans and cotton.

The two-spotted spider mite, Tetranychus urticae Koch, is an important pest of cotton in mid-southern USA and causes yield reduction and deprivation in fiber fitness. Cotton and pinto beans grown in the greenhouse were infested with spider mites at the three-leaf and trifoliate stages, respectively. Spider mite damage on cotton and bean canopies expressed as normalized difference vegetation index indicative of changes in plant health was measured for 27 consecutive days. Plant health decreased incrementally for cotton until day 21 when complete destruction occurred. Thereafter, regrowth reversed decline in plant health. On spider mite treated beans, plant vigor plateaued until day 11 when plant health declined incrementally. Results indicate that pinto beans were better suited as a host plant than cotton for rearing T. urticae in the laboratory.

Nitrogen management in wheat based on the normalized difference vegetation index (NDVI) / Manejo do nitrogênio em trigo baseado no índice de vegetação por diferença normalizada (NDVI)

ABSTRACT: Biomass production and nitrogen (N) accumulated in wheat shoots may be used for quantifying optimal topdressing nitrogen doses. The objective of this study was to develop and validate models for estimating the amount of biomass and nitrogen accumulated in shoots and the N topdressing dose of maximum technical efficiency in wheat using the normalized difference vegetation index (NDVI) measured by an active optical canopy sensor. Field experiments were carried out in two years and treatments consisted of N doses applied at plant emergence and as topdressing. NDVI, shoot biomass and N accumulated in shoots at the growth stage of six fully expanded leaves and grain yield were evaluated, being determined the topdressing N dose of maximum technical efficiency (DMTE). The NDVI was positively correlated to shoot biomass and N content in shoots and models for the relationship between these variables were developed and validated. The DMTE was negatively correlated with the NDVI value evaluated at the moment of N topdressing application. Thus, NDVI evaluation by an active optical canopy sensor can be used for nitrogen fertilization in variable rate, allowing the adjustment of applied N doses in different areas within a field.

RESUMO: A produção de biomassa e o conteúdo de nitrogênio (N) acumulado na parte aérea de trigo podem ser utilizados na quantificação da dose ótima de N em cobertura. O objetivo deste estudo foi desenvolver e validar modelos para a estimativa das quantidades de biomassa e nitrogênio acumulado na parte aérea e a dose de máxima eficiência técnica de N em cobertura em trigo utilizando o Índice de vegetação por diferença normalizada (NDVI) medido por sensor óptico ativo de dossel. Experimentos foram conduzidos a campo, em dois anos, e os tratamentos constaram de doses de N aplicadas na emergência das plantas e em cobertura. Foram avaliados o NDVI, a biomassa e a quantidade de N acumulada na parte aérea no estádio de seis folhas completamente expandidas e o rendimento de grãos, sendo determinada a dose de máxima eficiência técnica de N em cobertura (DMET). O NDVI apresentou correlação positiva com a biomassa e quantidade de N acumulada na parte aérea e modelos para as relações entre estas variáveis foram propostos e validados. A DMET correlacionou-se negativamente com o valor de NDVI avaliado no momento da aplicação de nitrogênio em cobertura. Assim, a avaliação do NDVI por sensor óptico ativo de dossel pode ser utilizada para a adubação nitrogenada em taxa variável, permitindo o ajuste da dose de N aplicada em diferentes locais da lavoura.

In-Season Yield Prediction of Cabbage with a Hand-Held Active Canopy Sensor.

Efficient and precise yield prediction is critical to optimize cabbage yields and guide fertilizer application. A two-year field experiment was conducted to establish a yield prediction model for cabbage by using the Greenseeker hand-held optical sensor. Two cabbage cultivars (Jianbao and Pingbao) were used and Jianbao cultivar was grown for 2 consecutive seasons but Pingbao was only grown in the second season. Four chemical nitrogen application rates were implemented: 0, 80, 140, and 200 kg·N·ha-1. Normalized difference vegetation index (NDVI) was collected 20, 50, 70, 80, 90, 100, 110, 120, 130, and 140 days after transplanting (DAT). Pearson correlation analysis and regression analysis were performed to identify the relationship between the NDVI measurements and harvested yields of cabbage. NDVI measurements obtained at 110 DAT were significantly correlated to yield and explained 87-89% and 75-82% of the cabbage yield variation of Jianbao cultivar over the two-year experiment and 77-81% of the yield variability of Pingbao cultivar. Adjusting the yield prediction models with CGDD (cumulative growing degree days) could make remarkable improvement to the accuracy of the prediction model and increase the determination coefficient to 0.82, while the modification with DFP (days from transplanting when GDD > 0) values did not. The integrated exponential yield prediction equation was better than linear or quadratic functions and could accurately make in-season estimation of cabbage yields with different cultivars between years.

Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil / Índice de vegetação por diferença normalizada obtido por sensor remoto de superfície para caracterização do ciclo de videiras no Rio Grande do Sul, Brasil

ABSTRACT The normalized difference vegetation index (NDVI) obtained by remote sensing is widely used to monitor annual crops but few studies have investigated its use in perennial fruit crops. The aim of this study was to determine the temporal NDVI profile during grapevine cycle in vineyards established in horizontal training systems. NDVI data were obtained by the ground-based remote sensing Greenseeker in Chardonnay and Cabernet Sauvignon vineyards located in the Serra Gaúcha region, Rio Grande do Sul, Brazil, from September to June in the 2014/2015 and 2015/2016 vegetative seasons. The grapevine canopies were managed in horizontal training systems (T-trellis and Y-trellis). The results indicated that the temporal NDVI values varied during the grapevine cycle (0.33 to 0.85), reflecting the changing in vigor and biomass accumulation that resulted from the phenological stages and management practices. The temporal NDVI profiles were similar to both horizontal training systems. The NDVI values were higher throughout the cycle for Cabernet Sauvignon compared to Chardonnay indicating Cabernet Sauvignon as the cultivar with greater vegetative vigor. The NDVI obtained by ground-based remote sensing is a fast and non-destructive tool to monitor and characterize the canopy in real time, compiling into a single data several parameters related to vine development, like meteorological conditions and management practices that are difficult to be quantified together.

RESUMO O índice de vegetação por diferença normalizada (NDVI), obtido por sensoriamento remoto, tem sido amplamente empregado no monitoramento de culturas agrícolas produtoras de grãos, porém poucos são os estudos em fruticultura. O objetivo deste trabalho foi caracterizar a evolução temporal do NDVI obtido por sensor remoto de superfície ao longo do ciclo de videiras em sistemas horizontais de condução do dossel vegetativo. Dados de NDVI foram obtidos com sensor remoto Greenseeker em vinhedos na região da Serra Gaúcha, Rio Grande do Sul, Brasil, de setembro a junho nas safras 2014/2015 e 2015/2016. Os vinhedos das cultivares Chardonnay e Cabernet Sauvignon eram conduzidos em sistema horizontal (latada e lira). Os resultados indicaram que houve variabilidade temporal do NDVI ao longo do ciclo (de 0,33 a 0,85), a qual refletiu as alterações no acúmulo de biomassa e vigor vegetativo decorrentes das principais etapas fenológicas e práticas de manejo. A evolução temporal do NDVI foi semelhante nos sistemas latada e lira, ambos caracterizados pela condução horizontal do dossel vegetativo. Os valores de NDVI para 'Cabernet Sauvignon' foram superiores aos de 'Chardonnay' ao longo do ciclo, independe da safra avaliada e do sistema de condução, indicando 'Cabernet Sauvignon' como a cultivar de maior vigor vegetativo. O NDVI, obtido por sensor remoto de superfície, é uma forma rápida e não destrutiva de monitoramento e caracterização do dossel vegetativo em tempo real, compilando em uma única informação o desenvolvimento da videira, o qual é resultado de diversos fatores, edafo-climáticos e de manejo, dificilmente quantificados conjuntamente.

Adaptive nitrogen and integrated weed management in conservation agriculture: impacts on agronomic productivity, greenhouse gas emissions, and herbicide residues.

Increasing nitrogen (N) immobilization and weed interference in the early phase of implementation of conservation agriculture (CA) affects crop yields. Yet, higher fertilizer and herbicide use to improve productivity influences greenhouse gase emissions and herbicide residues. These tradeoffs precipitated a need for adaptive N and integrated weed management in CA-based maize (Zea mays L.)-wheat [Triticum aestivum (L.) emend Fiori & Paol] cropping system in the Indo-Gangetic Plains (IGP) to optimize N availability and reduce weed proliferation. Adaptive N fertilization was based on soil test value and normalized difference vegetation index measurement (NDVM) by GreenSeeker™ technology, while integrated weed management included brown manuring (Sesbania aculeata L. co-culture, killed at 25 days after sowing), herbicide mixture, and weedy check (control, i.e., without weed management). Results indicated that the 'best-adaptive N rate' (i.e., 50% basal + 25% broadcast at 25 days after sowing + supplementary N guided by NDVM) increased maize and wheat grain yields by 20 and 14% (averaged for 2 years), respectively, compared with whole recommended N applied at sowing. Weed management by brown manuring (during maize) and herbicide mixture (during wheat) resulted in 10 and 21% higher grain yields (averaged for 2 years), respectively, over the weedy check. The NDVM in-season N fertilization and brown manuring affected N2O and CO2 emissions, but resulted in improved carbon storage efficiency, while herbicide residuals in soil were significantly lower in the maize season than in wheat cropping. This study concludes that adaptive N and integrated weed management enhance synergy between agronomic productivity, fertilizer and herbicide efficiency, and greenhouse gas mitigation.

Estimativa do potencial produtivo em trigo utilizando sensor óptico ativo para adubação nitrogenada em taxa variável / Wheat yield potential estimation using active optical sensor for site-specific nitrogen fertilization

A adubação nitrogenada em trigo é baseada no potencial produtivo da cultura, teor de matéria orgânica do solo e cultura antecessora. A definição do potencial produtivo é complexa, pois este varia com as condições meteorológicas de cada ano específico. O objetivo deste trabalho foi avaliar a relação entre o índice de vegetação por diferença normalizada (NDVI), medido por sensor óptico ativo e o rendimento de grãos em quatro cultivares de trigo, visando a desenvolver procedimentos para a adubação nitrogenada em cobertura em taxa variável. O experimento foi realizado em campo em 2009. Foram avaliados o NDVI em diferentes estádios de desenvolvimento e o rendimento de grãos. As leituras do NDVI ao longo do ciclo ativo foram eficientes em identificar variações de produtividade do trigo. Assim, o potencial de produtividade pode ser estimado através de medições desse índice durante a ontogenia da planta. Pode-se adotar um modelo único para descrever a relação entre NDVI e potencial produtivo para as cultivares testadas neste trabalho.

Nitrogen fertilization in spring wheat is based on yield potential, soil organic matter content and previous crop. Yield potential definition is difficult, since it is affected by weather conditions. The objective of this research was to evaluate the relationship between Normalized Difference Vegetation Index (NDVI) measured by an active sensor and grain yield of four wheat cultivars. The experiment was carried out at field conditions in 2009. NDVI in different growth stages and grain yield were evaluated. NDVI measured was efficient to detect growth variability generated by N availability and correlated well with grain yield for all cultivars tested, indicating that yield potential can be estimated by NDVI evaluations during crop ontogeny. One single model for the relationship between NDVI and yield potential can be used considering cultivars used in this research.