► Identifying genetic markers for yield requires rapid quantification of crop traits. ► Proximal sensing offers promise for field-based phenotyping (FBP). ► Efficient data integration and ...modeling-assisted analysis are key for FBP. ► FBP scaled to thousands of field plots is a feasible, attainable goal. ► FBP systems require new, integrative collaborations that cross disciplines.
A major challenge for crop research in the 21st century is how to predict crop performance as a function of genetic architecture. Advances in “next generation” DNA sequencing have greatly improved genotyping efficiency and reduced genotyping costs. Methods for characterizing plant traits (phenotypes), however, have much progressed more slowly over the past 30 years, and constraints in phenotyping capability limit our ability to dissect the genetics of quantitative traits, especially those related to harvestable yield and stress tolerance. As a case in point, mapping populations for major crops may consist of 20 or more families, each represented by as many as 200 lines, necessitating field trials with over 20,000 plots at a single location. Investing in the resources and labor needed to quantify even a few agronomic traits for linkage with genetic markers in such massive populations is currently impractical for most breeding programs. Herein, we define key criteria, experimental approaches, equipment and data analysis tools required for robust, high-throughput field-based phenotyping (FBP). The focus is on simultaneous proximal sensing for spectral reflectance, canopy temperature, and plant architecture where a vehicle carrying replicated sets of sensors records data on multiple plots, with the potential to record data throughout the crop life cycle. The potential to assess traits, such as adaptations to water deficits or acute heat stress, several times during a single diurnal cycle is especially valuable for quantifying stress recovery. Simulation modeling and related tools can help estimate physiological traits such as canopy conductance and rooting capacity. Many of the underlying techniques and requisite instruments are available and in use for precision crop management. Further innovations are required to better integrate the functions of multiple instruments and to ensure efficient, robust analysis of the large volumes of data that are anticipated. A complement to the core proximal sensing is high-throughput phenotyping of specific traits such as nutrient status, seed composition, and other biochemical characteristics, as well as underground root architecture. The ability to “ground truth” results with conventional measurements is also necessary. The development of new sensors and imaging systems undoubtedly will continue to improve our ability to phenotype very large experiments or breeding nurseries, with the core FBP abilities achievable through strong interdisciplinary efforts that assemble and adapt existing technologies in novel ways.
Assessing nitrogen (N) status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using ...statistical analysis approach where they are often subject to site-specific problems. This study describes and tests a novel method of utilizing physical characteristics of N-fertilized cotton and combining field spectral measurements made at different spatial scales as an approach to estimate in-season chlorophyll or leaf N content of field-grown cotton. In this study, leaf greenness estimated from spectral measurements made at the individual leaf, canopy and scene levels was combined with percent ground cover to produce three different indices, named TCCLeaf, TCCCanopy, and TCCScene. These indices worked best for estimating leaf N at early flowering, but not for chlorophyll content. Of the three indices, TCCLeaf showed the best ability to estimate leaf N (R2 = 0.89). These results suggest that the use of green and red-edge wavelengths derived at the leaf scale is best for estimating leaf greenness. TCCCanopy had a slightly lower R2 value than TCCLeaf (0.76), suggesting that the utilization of yellow and red-edge wavelengths obtained at the canopy level could be used as an alternative to estimate leaf N in the absence of leaf spectral information. The relationship between TCCScene and leaf N was the lowest (R2 = 0.50), indicating that the estimation of canopy greenness from scene measurements needs improvement. Results from this study confirmed the potential of these indices as efficient methods for estimating in-season leaf N status of cotton.
ABSTRACT
Simple and rapid methods are needed to measure durum wheat (Triticum durum L.) nitrogen (N) status and make on‐site N application decisions for increased crop yield and grain quality. ...Although chlorophyll meters (SPAD meters) have been widely tested for cereal crop N management, significant variation in SPAD meter readings among growing seasons, locations, and crop cultivars makes them challenging. Experiments with six durum wheat cultivars and six N fertilizer rates were conducted in Arizona in the 2010–2011 and 2011–2012 growing seasons to test whether multiple leaf SPAD readings on the same plants can improve estimation of crop N status by SPAD meters. The relationships between N nutrition index (NNI) and SPAD readings on the most recent fully expanded leaves (SPAD1), Sufficiency Index or normalized SPAD index (SI), Normalized difference SPAD index (NDSPAD), and the differences in SPAD readings between the second most recent and most recent fully expanded leaves (SPAD21) were compared. The results showed SPAD1 varied with growing season, growth stage, and durum wheat cultivar. All three indices, SI, NDSPAD, and SPAD21, improved the prediction of durum wheat N status compared to SPAD1. The SI measured at Feekes 10.5 or mean SI over growth stages (Feekes 5, 10, and 10.5) performed better than the other three indices in predicting crop yield. This study suggests that using SPAD21 can improve the effectiveness of the SPAD meter compared to SPAD1 and that SPAD21 can be as effective as SI without requirement of reference plots in durum wheat N management.
Understanding spatial and temporal variability in crop yield is a prerequisite to implementing site-specific management of crop inputs. Apparent soil electrical conductivity (EC
a
), soil brightness, ...and topography are easily obtained data that can explain yield variability. The objectives of this study were to evaluate the spatial and temporal variability in cotton (
Gossypium hirsutum
L.) yield and determine the relationship between yield and soil EC
a
, topography, and bare soil brightness at a field level in multiple growing seasons. A 50-ha field grown with cotton from 2000 to 2003 and 2005 on the Southern High Plains of Texas was selected for this study. Yield was negatively correlated with bare soil brightness (−0.47 <
r
< −0.33 for red band) and positively correlated with EC
a
(0.08 <
r
< 0.29 for 30-cm EC
a
and 0.28 <
r
< 0.44 for 90-cm EC
a
). Yield had stronger correlation with relative elevation and slope than with profile curvature and planar curvature. Combined, EC
a
, topographic attributes, and bare soil brightness explained up to 70.1 % of cotton yield variability. Bare soil brightness and EC
a
were strongly related to soil texture. Brighter soils with low EC
a
values had lower clay content. Yield and soil properties had stronger correlation in dry growing seasons than in wet growing seasons. Cotton yield variability pattern was relatively stable across different growing seasons. Soil texture was one of the greatest factors influencing cotton yield variability. Results of this study provide a basis for site-specific management of yield goals and variable rate application of water, fertilizers, seeds, and other inputs.
ABSTRACT
Optimizing N management and using cultivars with high N use efficiency (NUE) are important for durum wheat (Triticum durum Desf.) producers in irrigated desert production systems. A field ...experiment with six durum wheat cultivars was conducted with five or six levels of N treatment for two growing seasons (2010–2011 and 2011–2012) to determine NUE and yield of durum wheat cultivars under irrigated desert conditions. Recovery efficiency of N fertilizer was very high, ranging from 63 to 79%. Durum wheat cultivars differed in grain N concentration (GNC) in both seasons. In the 2011 to 2012 growing season, cultivars differed in grain yield, agronomic efficiency (AE), NUE, and N harvest index (NHI). The N fertilizer rate had a highly significant, positive effect on nearly all variables in both seasons. As the N fertilizer rate increased, grain yield, GNC, and total N uptake increased and then plateaued, while AE and N utilization efficiency (NUtE) decreased linearly. Nitrogen uptake efficiency (NUpE) and NUE decreased as a power function with increased N fertilizer rates. Durum wheat NUE was more closely related to NUpE than NUtE. The N harvest index responded in a quadratic manner to N rate in both years. In summary, although there was variation in grain yield and NUE among durum wheat cultivars, N fertilizer effects were much more significant than cultivar effects. This indicates that grower decisions on optimal N rate will have more impact than cultivar selection.
Overapplication of N in cereal crops leads to low N recovery efficiency and risk of NO3 pollution of ground water. The chlorophyll meter, also known as SPAD meter, is a simple, portable diagnostic ...tool for identifying crop N status. We used it to test need‐based N management approaches for rice (Oryza sativa L.) and wheat (Triticum aestivum L.) on a loamy sand in northwestern India. Applying 30 kg N ha−1 each time the SPAD value fell below the critical value of 37.5 resulted in application of 90 kg N ha−1, which produced rice yields equivalent to those with 120 kg N ha−1 applied in three splits. Using a SPAD value of 35 was inadequate for the two rice cultivars because it resulted in application of only 60 kg N ha−1 and, thus, low yields. With high inherent soil fertility resulting in rice yield of >3 Mg ha−1 in zero‐N plots, applying N basally or a week after rice transplanting did not further increase yield. Limited experimentation with leaf color chart (LCC) indicated that N management based on LCC shade 4 helped avoid overapplication of N to rice. Wheat responded to N application at maximum tillering (MT) when SPAD value fell below 44. Wheat yield increased by 20% when 30 kg N ha−1 was applied at SPAD value of 42 at MT. Results show that plant need–based N management through chlorophyll meter reduces N requirement of rice from 12.5 to 25%, with no loss in yield.
Nitrate (NO3) profiles in semiarid unsaturated zones archive land use change (LUC) impacts on nitrogen (N) cycling with implications for agricultural N management and groundwater quality. This study ...quantified LUC impacts on NO3 inventories and fluxes by measuring NO3 profiles beneath natural and rainfed (nonirrigated) agricultural ecosystems in the southern High Plains (SHP). Inventories of NO3N under natural ecosystems in the SHP normalized by profile depth are extremely low (2−10 kg NO3N/ha/m), in contrast to those in many semiarid regions in the southwestern U.S. Many profiles beneath cropland (9 of 19 profiles) have inventories at depth that range from 28−580 kg NO3N/ha/m (median 135 kg/ha/m) that correspond to initial cultivation, dated using soil water Cl. These inventories represent 74% (median) of the total inventories in these profiles. This NO3 most likely originated from cultivation causing mineralization and nitrification of soil organic nitrogen (SON) in old soil water (precultivation) and is attributed to enhanced microbial activity caused by increased soil wetness beneath cropland (median matric potential −42 m) relative to that beneath natural ecosystems (median −211 m). The SON source is supported by isotopes of NO3 (δ15N: +5.3 to +11.6; δ18O: +3.6 to +12.1). Limited data in South Australia suggest similar processes beneath cropland. Mobilization of the total inventories in these profiles caused by increased drainage/recharge related to cultivation in the SHP could increase current NO3N levels in the underlying Ogallala aquifer by an additional 2−26 mg/L (median 17 mg/L).
A standardized experiment was conducted during 2009 and 2010 at 20 location‐years across U.S. cotton (Gossypium hirsutum L.)‐producing states to compare the N use requirement of contemporary cotton ...cultivars based on their planting seed size. Treatments consisted of three cotton varieties with planting seed of different numbers of seed per kg and N rates of 0, 45, 90, and 134 kg ha–1. Soil at each trial location was sampled and tested for nitrate presence. High levels of soil nitrate (>91 N‐NO3– kg ha–1) were found in Arizona and western Texas, and soil nitrate in the range of 45 to 73 kg N‐NO3– ha–1 was found at locations in the central United States. Cotton lint yield responded to applied N at 11 of 20 locations. Considering only sites that responded to applied N, highest lint yields were achieved with 112 to 224 kg ha–1of applied plus pre‐plant residual soil NO3—translating to an optimal N requirement of 23 kg ha–1 per 218 kg bale of lint produced. Among the varieties tested those with medium‐sized seed produced higher yields in response to N than did larger and smaller seeded varieties. Varieties with larger seed had longer and stronger fibers, higher fiber length uniformity than small seeded varieties and decreased micronaire. Seed protein and oil increased and decreased slightly in response to increasing amounts of soil nitrate plus applied N, respectively.
Nitrogen fertilizer management in subsurface drip irrigation (SDI) systems for cotton (Gossypium hirsutum L.) can be very efficient when N is injected with the irrigation water (fertigated) on a ...daily basis. However, the daily rates and total amounts of N fertigation are uncertain. Normalized difference vegetative index (NDVI), calculated from weekly canopy reflectance measurements can guide N management in SDI cotton. The objective of this 3‐yr study (2007–2009) on an Acuff sandy clay loam (fine‐loamy, mixed, superactive, thermic Aridic Paleustolls) near Lubbock, TX, was to test two canopy reflectance‐based strategies for estimating and adjusting injection rates of urea ammonium nitrate (UAN) fertilizer between first square and early mid‐bloom. We also evaluated three N rates; 50, 100, and 150% of the soil test‐based N recommendation for a 1400 kg lint ha−1 yield goal. In the reflectance‐based N strategy‐1 (RN1), UAN was injected starting at first square at 50% of the soil test N rate. When NDVI in RN1 fell significantly below NDVI of plots with 100% soil test N, the N injection rate was increased to match the injection rate of the 100% soil test plots. The reflectance‐based N strategy‐2 (RN2) had an initial N injection rate equal to that of the 100% soil test N, and was raised to match the 150% soil test N based on NDVI. Nitrogen rates for the RN1 averaged across 3 yr were 22 kg N ha−1 less, or 31% less than the soil test treatment, without hurting lint or seed yields. In 2007, N rates with RN2 were 11 kg N ha−1 higher than the soil test N rate, without any yield benefit. Economic optimum N rates for lint production ranged from 23 kg N ha−1 in 2009 to 75 kg N ha−1 in 2008.
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