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Irrigation management is one of most critical factors influencing soil N

Injudicious nitrogen (N) fertilizer application has increased the risk of environmental pollution and decreased grain yields, farm profits, and N use efficiency. The plant based N diagnostic tools can be used to optimize N management of summer maize production. This study was designed to develop and validate a leaf area index (LAI) based critical N (Nc) dilution curve and to establish a theoretical framework to link the relationship of LAI and plant dry matter (DM) based curves, as well as to compare the differences between LAI, plant DM and growth stage-based Nc curves. Six field experiments were set with a range of N application rates (0–320 kg N ha−1) and plant densities (6 to 8 × 104 plants ha-1) using four summer maize cultivars (Zhengdan958, Denghai605, Xundan20, and Denghai661) in Henan province of China. LAI and plant N concentration (PNC) were determined from V6 to R1 stages in each experiment for the development of the Nc curve. LAI and PNC ranged from 1.28 to 6.12 and 1.34% to 3.31% under different N levels, respectively. Allometric relationships between LAI, plant DM, and critical N uptake (Nuc) were developed under non-N-limiting treatments. The relationship between Nc and LAI during vegetative growth period was described by a power function (Nc = 3.84LAI−0.45). N nutrition index (NNI) increased with the increasing N application rate and ranged from 0.56 and 1.23 across different N treatments. Our results validated that plant N uptake was proportional with LAI, and the allometric parameter between LAI and plant DM of summer maize was close to the theoretical value 2/3. The newly developed LAI-based Nc curve could identify plant N status (N-limiting and non-N-limiting) during key N requirement period of summer maize and can be used for precision N management for summer maize grown in China.

Accurately and timely diagnosis of plant nitrogen (N) status is imperative for N fertilization management and yield prediction of summer maize. This study was aimed to identify the most sensitive/appropriate spectral band combinations to estimate the N nutrition index (NNI) by comprehensive analyses on canopy spectral reflectance from visible to near-infrared light, to develop the optimum vegetation indices for NNI during V6-V12 growth period, and to validate the regression models for estimating NNI of summer maize by comparing the two methods (direct and indirect) to determine the most appropriate method for practical use. Five multi-locational and multi-N rates (0–320 kg ha−1) field experiments were conducted during three growing seasons (2015, 2016 and 2017) using five summer maize cultivars. The measurements regarding canopy spectral reflectance, plant biomass, and plant N concentration were taken at critical stages of summer maize under the various N treatments. Comprehensive analyses on the different regression models of NNI for normalized difference spectral index (NDSI) and ratio spectral index (RSI) composed of any two bands between 325 and 905 nm of summer maize were made by using the reduced precise sampling method. The NNI values in the present study ranged from 0.68 to 1.15 under different N treatments. The most sensitive spectral bands were located at 710 nm (red edge band) and 512 nm (visible light band) and the optimum spectral vegetation index for estimating NNI was NDSI (R710, R512). The linear regression model between NDSI (R710, R512) and NNI was NNI = 0.95 NDSI (R710, R512) + 0.14. Additionally, the soil-adjusted vegetation index (SAVI) was used to correct NDSI(R710, R512), and the performance of the linear regression model was best when the parameter L (soil-brightness correction factor) of SAVI (R710, R512) was 0.05. The performances of the direct and indirect NNI estimation methods were compared. The validation results showed that the performance of the newly developed vegetation indices (NDSI (R710, R512) and SAVI (R710, R512)(L=0.05)) was the best with the relative root mean square error (RRMSE) values ranging from 11.4% and 13.1% in the direct method; while the performance of the existing vegetation indices (Ratio Vegetation Index II and modified SAVI) were best with RRMSE value of 16.9% in the indirect method. It was concluded that both the direct and indirect methods can be used to estimate NNI of summer maize, but the construction of the newly developed vegetation indices was easier in the direct method. The projected results will provide a technical basis for potential application of remote sensing technology for monitoring and diagnosis of plant N nutrition in summer maize production.

Ear nitrogen (N) accumulation plays an imperative role in ear photosynthesis and the formation of grain yield and quality of maize and wheat. However, the mechanism governing ear N accumulation (NAe) especially under different N levels is not yet clear. For this purpose, this study endeavored to describe the N source-sink ratio of NAe by developing a model using ear critical N concentration (%Nc) dilution curves of maize and wheat. The data obtained include ear dry matter (DMe), NAe, ear N nutrition index (NNIe), ear N accumulation rate index (NARIe), post-anthesis N uptake (PANU), grain number (GN), and thousand-grain weight (TGW) were collected from 12 varied N rates (0–300 kg N ha−1) field experiments conducted using four wheat and three maize cultivars. Integrated models were developed to show the effect of N source and sink on NAe in maize and wheat. The ratio between PANU and NAe was used to represent the post-anthesis supply capacity of N source, while DMe was used as the storage capacity of N sink in these models. Robust relationships of NARIe with the ratio between PANU and NAe in maize and wheat indicated that NAe was controlled by N source from N-limiting to non-N-limiting levels. The relationship between the rate of ear dry matter accumulation (DMRe) and NNIe showed a linear plateau model across different N levels due to the similar DMe under non-N-limiting levels, indicating that ear N status was regulated by DMe (N sink) under N-limiting levels. Therefore, NAe under N-limiting levels was co-regulated by N source and sink. Besides, the temporal effect of plant N status before anthesis on NAe was also observed. The close relationship between DMe and plant N status at anthesis across different N levels in maize and wheat indicated that the potential ear N sink was controlled by the pre-anthesis supply capacity of plant N source. Consequently, pre-anthesis plant N status was imperative for quantifying potential ear N sink. This was the first-ever attempt to investigate the N source-sink ratio of NAe using ear %Nc curve and highlighted the effect of pre-anthesis plant N status on NAe. The findings will contribute towards a better understanding of the process of NAe under different N levels for developing a judicious N management strategy for improving the N use efficiency of wheat and maize.

Accurate diagnosis of nitrogen (N) fertilizer required for crop growth can serve as a guide for N management by improving N use efficiency and grain yields. The critical N concentration (Nc), the minimum N required for maximal crop growth has been widely used to determine crop N status. Nc dilution curves have been determined in several crops including summer maize on plant dry matter (DM) basis, yet no attempt has been made to determine the Nc dilution curve on the basis of leaf dry matter (LDM) in summer maize. The present study aimed to determine a Nc dilution curve based on LDM for in-season assessment of crop N status in summer maize. Six field experiments were performed with four summer maize cultivars using varied N fertilizer rates ranging from 0 to 320 kg N ha−1. The leaf Nc curve was described by the equation: Nc = 3.45LDM−0.22, when LDM ranged from 1.18 to 3.45 t ha−1. For LDM

Rapid, accurate and dynamic diagnosis of nitrogen (N) status is essential for evaluating plant vigor, predicting crop production and optimizing N management in winter barley (Hordeum vulgare L.). The objectives of this study were to determine the correlations between N nutrition index (NNI), leaf N gradient and chlorophyll meter (CM) readings gradient for different leaf positions and to compare the stability of the relationships between NNI and CM readings as well as between NNI and positional differences chlorophyll measurements index (PDCMI) in different cultivars and environments. Four multi-locational field experiments using five winter barley cultivars (Supi6, Yangpi4, Yangnongpi8, Supi2, Dan2) and varied N rates (0–300 kg ha−1) were conducted in this study. NNI, leaf N gradient, CM readings, CM readings gradient and PDCMI were determined for growth analyses from Feekes 6 to Feekes 10.51. Our results represented that the NNI and CM readings increased with increasing N application rates. In contrast, the PDCMI decreased with increasing N application rates. Further, the leaf N gradient of the canopy was not uniform, the lower leaf position could better reflect N status in winter barley plants, and the CM readings gradient was a good proxy of leaf N gradient. The CM readings at different leaf positions showed a significantly positive relatation to NNI, yet the relationship varied among cultivars and seasons. In contrast, the PDCMI of different leaves showed significantly negative relation to NNI, with the exception of PDCMI12 and PDCMI13. The strongest correlation between NNI and PDCMI was found for PDCMI14 (NNI = −1.927 × PDCMI14 + 1.17, R = –0.838**), which was stable across the cultivars and seasons. Validation of the relationships with independent data produced a root mean squared error (RMSE) of 0.13 between the predicted and observed NNI values. This robust and stable relationship between PDCMI14 and NNI could be used as a reliable tool to diagnose plant N status of winter barley in eastern China.

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