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Nitrogen is the most limiting nutrient for crop production in many of the world's agricultural areas and its efficient use is important for the economic sustainability of cropping systems Furthermore, the dynamic nature of N and its propensity for loss from soil‐plant systems creates a unique and challenging environment for its efficient management Crop response to applied N and use efficiency are important criteria for evaluating crop N requirements for maximum economic yield Recovery of N in crop plants is usually less than 50% worldwide Low recovery of N in annual crop is associated with its loss by volatilization, leaching, surface runoff, denitrification, and plant canopy Low recovery of N is not only responsible for higher cost of crop production, but also for environmental pollution Hence, improving N use efficiency (NUE) is desirable to improve crop yields, reducing cost of production, and maintaining environmental quality To improve N efficiency in agriculture, integrated N management strategies that take into consideration improved fertilizer along with soil and crop management practices are necessary Including livestock production with cropping offers one of the best opportunities to improve NUE Synchrony of N supply with crop demand is essential in order to ensure adequate quantity of uptake and utilization and optimum yield This paper discusses N dynamics in soil‐plant systems, and outlines management options for enhancing N use by annual crops

Enhancing nitrogen use efficiency in crop plants

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

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Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Physiological perspectives of changes over time in maize yield dependency on nitrogen uptake and associated nitrogen efficiencies: A review

Remote-sensing techniques, in particular, multispectral visible and infrared (IR) reflectance, can provide Correlations between plant canopy reflectance and aboveground an instantaneous, nondestructive, and quantitative asbiomass can possibly be used for early prediction of crop yield. Field experiments were conducted in 1998 and 1999 on two soil types to sessment of the crop’s ability to intercept radiation and assess whether measurements of canopy reflectance at given stages photosynthesize (Ma et al., 1996). The input of reflecof development could be used to discriminate high from low potential tance into yield production models has been shown to yields among genotypes with known differences in potential grain improve yield estimates (Clevers et al., 1994; Clevers, yield and whether a consistent relationship between yield and canopy 1997). Colwell (1956) was the first to use aerial IR phoreflectance could be used for screening and predicting soybean [Gly- tographs to monitor plant disease in the field. The cine max (L.) Merr.] yield in a variety trial. A 3-by-42 factorial experi- amount of reflectance in the near IR (NIR) range ( ment, arranged in a randomized complete block design with three 700–1300 nm) is determined by the optical properties replications, was used on each soil type for both years. Three populaof the leaf tissues: their cellular structure and the air–cell tion densities (25, 50, and 75 seeds m 2 ) represented low, optimum, wall–protoplasm–chloroplast interfaces (Kumar and Silva,

/pdf/early-prediction-of-soybean-yield-from-canopy-reflectance-3yvzr2a7fu.pdf

Early prediction of soybean yield from canopy reflectance measurements

The amount and distribution of leaf area and leaf angles in a crop canopy determine how photosynthetically active radiation (PAR) is intercepted and consequently influences canopy photosynthesis and yield. Factors such as plant shape, plant populations, and row width will affect these leaf distributions and can occur in an almost infinite number of different combinations. To supplement experimentation, a mathematical model was developed to use measurements of leaf area and leaf angles in two dimensions (with height and across the row) to calculate PAR interception and canopy photosynthesis. Maize (Zea mays L.) hybrids with phenotypic differences were planted at several plant populations to produce a wide range of two-dimensional leaf area and leaf angle patterns. The extreme phenotypes, leafy and reduced stature, were included to vary plant height and number of leaves above the ear. Measurements of average PAR at various levels were made in seven different canopies and compared with calculations from the model (R 2 of 0.68 and 0.92 for two sets of data). As well, measurements of PAR at 20-cm increments on transects perpendicular to the row were made in three canopy types at three levels and compared with theoretical calculations (R 2 = 0.74). A simple numerical experiment was run to demonstrate the utility of the model where daily canopy photosynthesis was calculated for two row widths and seven plant types. One result was that depending on row widths, plants with very upright leaves can have both the smallest and largest daily canopy photosynthesis.

Canopy structure, light interception, and photosynthesis in maize

generally show greater differences (Box and Ramseur, 1993). Several methods have been used to estimate root Image analysis has greatly simplified the measurement of root

Root Morphology of Contrasting Maize Genotypes

The SPAD chlorophyll meter was found to be a reliable, quick, and non-destructive tool used for directly measuring leaf chlorophyll and indirectly assessing the proportional parameter of leaf, and by extension, plant nitrogen (N) status. The meter has been used successfully to assess leaf N in conventional maize crops, but it has not been used with new maize (Zea mays L.) genotypes containing leafy (L) and reduced stature (RS) traits. SPAD meter readings were collected on the uppermost fully developed leaves (before silking) and on the ear leaf (after silking) of field grown maize genotypes with and without the L and RS traits. The experiment was conducted during 1996 and 1997 at two sites in Eastern Canada (Ottawa and Montreal). At each site in each year, a split plot arrangement of two treatment factors was used in a randomized complete block design with four blocks. The main plot treatments were levels of N (0, 85, 170, and 255 kg ha−1), with six maize genotypes as subplot treatments. The hybrids inclu...

Inter-relationships of applied nitrogen, spad, and yield of leafy and non-leafy maize genotypes

Measurement of relatively small (<100 m total length, <6 g fresh wt.) root systems has been simplified by image analysis, but measuring larger root systems remains time-consuming and inaccurate. Reliability of root estimation can be improved through identification of effective sampling methods. We devised a system for the collection of homogeneous root subsamples by air-stirring in water. We optimized the subsampling technique and used a scanner-based image analysis system to measure total root length, mean root diameter, and root surface area of three maize (Zea mays L.) genotypes with contrasting root morphologies: leafy reduced stature (LRS), leafy normal stature (LNS), and Pioneer 3905 (P3905), a commercial hybrid. Root length was determined for 957 subsamples. Confidence intervals were generated by software using the bootstrap resampling approach for optimizing sample size. Confidence intervals for mean estimates of each sample size were defined by ordering the evaluation function values from the smallest to the largest in a set of 5000 iterations. The lower and upper bounds of confidence intervals were also calculated using the standard procedure. This system allowed collection of homogeneous subsamples. Calculations showed that ∼10% of total root volume should be analyzed for estimation of the entire root system to be accurate within 10%. Although unreplicated, these data suggest that maize genotypes with the leafy trait have greater root lengths (1.75 km for LRS, 2.37 km for LNS, and 0.49 km for conventional commercial hybrid P3905) and a greater proportion of fine roots than the nonleafy type.

Carlos Eduardo Costa

Papers

Early prediction of soybean yield from canopy reflectance measurements

Canopy structure, light interception, and photosynthesis in maize

Root Morphology of Contrasting Maize Genotypes

Inter-relationships of applied nitrogen, spad, and yield of leafy and non-leafy maize genotypes

A Sampling Method for Measurement of Large Root Systems with Scanner-Based Image Analysis