Nitrogen Status Research Articles

Assessment of Nitrogen Nutrition Index of Winter Wheat Canopy from Visible Images for a Dynamic Monitoring of N Requirements

Hand-held chlorophyll meters or leaf-clip-type sensors indirectly and instantaneously measure leaf N content. They can provide an N nutrition index (NNI) value that is crucial information for adjusting the amount of N fertilizer to the actual N status of the plant. Although these measurements are non-invasive and non-destructive, they require numerous repetitions at the canopy scale. The objective of this work was to explore the potential of visible images to predict nitrogen status in winter wheat crops from estimating NNI and to compare these results with those deduced from classical methods. Based on a dark green colour index (DGCI), which combines hue, saturation and brightness, a normalized DGCI (nDGCI) was proposed as the ratio between the measurements of the study microplot and those of the over-fertilized microplot. The methodology was performed on winter wheat microplots with a nitrogen gradient. Half of the microplots were grown with a single cultivar (LG Absalon) and the other half with a mixture of four wheat cultivars. The impact of optical device (digital camera or smartphone), the white balance (Manual or Automatic), the crop growth stage (two-nodes or heading) and cultivars (single or mixed) on the relationship between (DGCI, nDGCI) and NNI was evaluated. The results showed a close correlation between the nDGCI values and the NNI_NTester values, especially on a single cultivar (LG Absalon; R2 = 0.73 up to 0.91 with smartphone). It suggested that the relationship is highly sensitive to the wheat cultivar. This approach with no specific calibration of images is promising for the estimation of N requirements in wheat field.

Symbiotic and Asymmetric Causality of the Soil Tillage System and Biochar Application on Soil Carbon Sequestration and Crop Production

Agriculture faces a significant challenge in maintaining crop production to meet the calorie demand of the ever-growing population because of limited arable land and climate change. This enforces a search for alternative multifarious agricultural-based solutions to meet the calorie demand. In search of alternatives, agricultural soil management has been highlighted and is expected to contribute to climate change mitigation through soil carbon sequestration and reduce greenhouse gas emissions through effective agricultural management practices. The addition of biochar to the soil significantly improves the soil nitrogen status, soil organic carbon, and phosphorus, with greater effects under the different tillage systems. This symbiosis association could further change the bacterial structure in the deeper soil layer which thus would be important to enhancing productivity, particularly in vertisols. Biochar also has an environmental risk and negative consequences. Heavy metals could be present in the final food products if we use contaminated raw materials to prepare biochar. However, there is a need to investigate biochar application under different climatic conditions, seasons, soil tillage systems, and crop types. These indicate that the positive effect of proper biochar fertilization on the physiology, yield formation, nutrient uptake, and soil health indicators substantiate the need to include biochar in the form of nutrients in the crop production sector, especially in light of the changing climate and soil tillage systems.

Response of Forest Ecosystems to Decreasing Atmospheric Nitrogen Deposition

Nitrogen deposition has serious consequences to global change. Excessive nitrogen deposition leads to nitrogen saturation in forests, resulting in soil acidification, nitrate leaching, an increase in nitrous oxide emissions, and a decrease in plant species diversity and vegetation productivity. Under the reduction of atmospheric nitrogen deposition in Europe, North America, and China, summarizing the response of forests to decreasing nitrogen deposition can not only improve the knowledge framework of the impact of nitrogen deposition on forests, but also evaluate the effects of emission abatement actions, as well as provide scientific basis for future air pollution control. This study reviewed the response of soil, surface water, nitrogen cycle, and vegetation of temperate forests in Europe and North America and subtropical forests in southwest China to the reduction in atmospheric nitrogen pollution gases and thus nitrogen deposition. The soil water nitrogen concentration responded rapidly to the nitrogen deposition reduction, although the trend was inconsistent. The soil acidification and nitrogen cycles showed a delayed response of recovery from high nitrogen deposition. The nitrogen mineralization and immobilization, soil carbon retention, and net primary production might take decades to respond to the nitrogen deposition reduction. However, the soil inorganic nitrogen pool and nitrogen leaching decreased with the decline in nitrogen deposition, although a one-or two-year lag existed. The surface water nitrogen concentration was closely related to nitrogen status in forests. After the nitrogen deposition decreased, the nitrogen leaching and thus the surface water nitrogen concentration decreased in the areas with historically high nitrogen deposition. However, the low surface water nitrogen concentration in the nitrogen-limited forests was not significantly affected by the nitrogen deposition changes. The recovery of surface water acidification was affected by soil sulfur desorption/mineralization and nitrification/denitrification. The foliar nitrogen concentration decreased with the decline in nitrogen deposition. The nitrogen-saturated forests and regional surface water in southwest China showed a recovery trend from high nitrogen deposition, as a consequence of the implementation of the Total Emissions Control of Air Pollutants and later the Action Plan of Air Pollution Prevention and Control.

Carbon and nitrogen status by decay class in fallen dead wood of three pine species in southern Korea

The importance of a quantitative assessment of C and N contents of dead wood is increasing in forest ecosystems. This study aimed to determine the density and carbon (C) and nitrogen (N) status of dead wood with decay class for three pine species (Pinus densiflora, Pinus rigida, and Pinus koraiensis) in southern Korea. The C concentration in dead wood was significantly different among species (P. densiflora, 50.31%; P. koraiensis, 47.22%; P. rigida, 44.96%), whereas decay class did not affect the C concentration (p > 0.05). The density and C content of dead wood in all species decreased with increasing decay class. The N concentrations of dead wood increased more rapidly in P. rigida and P. koraiensis than in P. densiflora, with an increasing decay class. Thus, the N content of dead wood was unchanged or increased in P. rigida and P. koraiensis, whereas that of P. densiflora decreased because of density reduction with increasing decay class. Our results indicate that the unchanged, increased, or decreased status of C and N in dead wood depends on the species and decay class.

Evaluation of Absolute Measurements and Normalized Indices of Proximal Optical Sensors as Estimators of Yield in Muskmelon and Sweet Pepper

The generally established protocol for leaf measurement with proximal optical sensors is to use the most recently fully expanded leaf. However, differences in the nitrogen (N) status of lower and upper leaves could possibly be used to enhance optical sensor measurement. Normalized indices that consider both upper and lower leaves have been proposed to improve the assessment of crop N status and yield estimation. This study evaluated whether normalized indices improved the estimation of crop yield from measurements with three different proximal optical sensors: (i) SPAD-502 leaf chlorophyll meter, (ii) Crop Circle ACS 470 canopy reflectance sensor, and (iii) Multiplex fluorescence meter. The study was conducted with sweet pepper (Capsicum annuum L.) and muskmelon (Cucumis melo L.) in plastic greenhouses in Almeria, Spain. Measurements were made on the latest (most recent) leaf (L1), and the second (L2), third (L3) and fourth (L4) fully expanded leaves. Yield estimation models, using linear regression analysis, were developed and validated from the absolute and normalized measurements of the three optical sensors. Overall, the calibration and validation results indicated that the absolute measurements generally had better yield estimation performance than the normalized indices for all the leaves and different leaf profiles. In both species, there was a better performance at the early phenological stages, such as the vegetative and flowering stages, for the absolute and normalized indices for the three optical sensors. Absolute proximal optical sensor measurements on the lower leaves (L2, L3 and L4) slightly improved yield estimation compared to the L1 leaf. Normalized indices that included the L4 leaf (L1–L4) had better yield estimation compared to those using L2 and L3 (e.g., L1–L2 and L1–L3). Of the normalized indices evaluated, the yield performance of the Relative Index (RI), Relative Difference Index (RDI), and Normalized Difference Index (NDI) were very similar, and generally superior to the Difference Index (DI). Overall, the results of this study demonstrated that for three different proximal optical sensors in both muskmelon and sweet pepper (i) normalized indices did not improve yield estimation, and (ii) that absolute measurements on lower leaves (L2, L3 and L4) slightly improved yield estimation performance.

Fall fertilization during nursery production increases nitrogen status of Purshia tridentata seedlings: implications for outplanting

During container nursery production of Purshia tridentata (antelope bitterbrush), we found that three fall fertilization applications successfully loaded plants with nitrogen (N) with little effect on plant biomass. Using 15N‐labeled fertilizer to track N movements, we observed that N‐loaded seedlings attained luxury consumption and ultimately translocated more N toward roots. In trees, such N‐loading efforts have been reported to increase seedling survival and growth after outplanting by improving initial root growth. This leads to enhanced access to water and nutrients that increases overall plant competitiveness and performance. Our results add, in general, to the paucity of information concerning fall fertilization of shrubs, and specifically that this technique offers promise to enhance the quality of antelope bitterbrush seedlings. This could improve overall planting success of this important shrub native to western United States rangelands. Increasing the effectiveness of planting antelope bitterbrush, which can often improve restoration trajectories more than direct seeding or reliance on natural regeneration of this species, could accelerate the pace and scope of critical habitat restoration. Restoration is needed as antelope bitterbrush abundance has been reduced across the landscape because of conversion to agriculture, invasion by nonnative annual grasses, and an increase in fire frequency and intensity, among other reasons. In addition, this species provides browse for ungulates and critical habitat for at‐risk species, such as Centrocercus urophasianus (greater sage‐grouse). Although N loading antelope bitterbrush has potential to improve outplanting performance, the resulting higher nutrient status of this preferred browse species may lead to elevated browsing during seedling establishment.

Late-season corn stalk nitrate measurements across the US Midwest from 2006 to 2018

The late-season Corn Stalk Nitrate Test (CSNT) is a well-known tool to help evaluate the after-the-fact performance of nitrogen management. The CSNT has the unique ability to distinguish between optimal and excessive corn nitrogen status, which makes it helpful for identifying the over-application of N so that farmers can adjust their future nitrogen decisions. This paper presents a multi-year and multi-location dataset of late-season corn stalk nitrate test measurements across the US Midwest from 2006 to 2018. The dataset consists of 32,025 corn stalk nitrate measurements from 10,675 corn fields. The nitrogen form, total N rate applied, US state, year of harvest, and climatic conditions are included for each corn field. When available, previous crop, manure source, tillage, and timing of N application are also informed. We provide a detailed description of the dataset to make it usable by the scientific community. Data are published through an R package and also available at the USDA National Agricultural Library Ag Data Commons repository and through an interactive website.

Modelling of the impact of climate change on the transformation of nitrogen forms in the soil and N2O emissions from the agroecosystems of Eastern Ukraine

The moisture-temperature regime influences the nitrogen status of the soil and the microbiological processes of the transformation of nitrogen forms. Therefore, we assumed that ongoing climate change may affect the emission of nitrous oxide N2O, which is formed in the course of the transformation of nitrogen forms in the soil and is one of the most aggressive greenhouse gases that determine the global warming potential. To test this hypothesis and make a quantitative assessment of the impact of climate change on the transformation of nitrogen forms in the soil and N2O emissions from the agroecosystem soils, we used a complex model of greenhouse gas emissions from an agroecosystem soil that we developed and the RCP4.5 climate change scenario. The research was performed for the chernozem soils of Eastern Ukraine, with winter wheat being the cultivated crop. ‘Dry’ and ‘wet’ years typical for the period of 2021–2050 were selected among climate conditions in accordance with the climate change scenario. A ‘dry’ year was considered to be a year with a precipitation of 60% or less of the long-term average, a ‘wet’ year was a year with a precipitation of 130% or more of the long-term average for the studied period. The level of ammonification during the growing season for both ‘dry’ and partially ‘wet’ years was mainly controlled by temperature. At the beginning of the period, at negative temperatures below –3 °C ammonification and nitrification almost stopped, and at temperatures above –2 °C they slightly increased. Indices for the intensity of these processes increased with a rise in temperature from 0 °C to 10–11 °C. In the conditions of a ‘dry’ year an increment in temperature to the level of 22.4–27.8 °C caused a sharp increase in the rate of ammonification and caused a high level of the rate of nitrification. For the conditions of a ‘wet’ year with a sufficiently low (compared to a ‘dry’ year) temperature regime, a relatively significant rise in temperature with still quite good humidification increased the intensity of the ammonification process. The dependence of the intensity of the ammonification and nitrification processes on the moisture reserves in the soil is traced. The high level of moisture reserves in the arable layer at the beginning of the growing season in both ‘dry’ and ‘wet’ years formed anaerobic conditions in the upper layer of the soil. Against the background of rising temperatures, this called forth the denitrification process. N2O emission was 0.03–0.29 g N-N2O/ha•day due to the denitrification process in a ‘dry’ year, and 0.7–5.2 g N-N2O/ha•day in a ‘wet’ year. The highest level of N2O emission due to nitrification was observed in a ‘dry’ year in the middle of the vegetation period at high temperatures (22.7–27.8 °С) and amounted to 8.2–11.2 g N-N2O/ha•day. A decrease in soil moisture reserves during the second half of the growing season reduced the level of N2O emissions. Nitrification was the main process producing N2O. The emissions of N2O with moisture reserves greater than 55 mm in the arable layer occurred due to denitrification. A rise in temperature increases the level of N2O emissions. The peculiarities of the influence of moisture and temperature conditions of ‘dry’ and ‘wet’ years on the processes of ammonification, nitrification, denitrification, and N2O emissions from chernozem soils according to the RCP4.5 climate change scenario in Eastern Ukraine were assessed.

Combining a multi-environment trial and a diagnosis method to assess potential yield and main limiting factors of three highly different pea types

The evaluation of varieties of cultivated species is based on the implementation of multi-environmental trials that show high performances or strong limitations in some environments. To know the cause of these limitations, and to reason the adaptation of varieties to different environments, we propose a combined approach between the classical analysis of experimental results and a diagnostic approach using the DiagVar tool, which allows to assess the impact of limiting factors in each trial, the potential yield and the sensitivity of cultivars to those factors. This procedure is applied in a large and diversified field trial network including eight locations and three years to the comparison of two to four cultivars of the three agronomical pea types: spring peas, hr-winter peas (non-reactive to photoperiod) and Hr-winter peas (reactive to photoperiod).Estimates of potential yields from DiagVar for the three pea types were consistent with the experimental results, with higher values for hr-winter cultivars than for spring and Hr-winter ones. We shed light on specific limiting factors for each type, some of them being rarely quantified previously, which allows us to better target specific regions or environments for each pea type. Thus, spring types were more impacted by early limiting factors (loss of plants, low temperatures and radiation, lack of water, leading to reduced growth) and end-of-cycle stresses (high temperatures, water stress). Diseases had higher impacts on both winter types. Here we show for the first time that the lack of solar radiation affected particularly hr-winter type and the low crop nitrogen status affected more frequently spring peas. These results highlight the interest of carrying out an agronomic diagnosis in the analysis of varietal multi-environment trials, revealing the most important limiting factors to take into account while breeding new pea cultivars, particularly limiting factors that are difficult to directly observe. This method proves to be reliable and informative for breeders, technicians and agricultural advisors to discuss the adaptation of varieties to specific environment.

Ammonia volatilization and nitrogen status in second-season corn after lime and gypsum application in no-till

In no-till (NT), liming and urea fertilization are performed on the soil surface, which can increase nitrogen (N) losses via ammonia volatilization. On the basis of N fertilization management, gypsum application provides a promising alternative for improving N uptake by plants. Therefore, the objective of this study was to evaluate the N behavior loss by NH3-N volatilization, the soil pH at a depth of 0 – 0.05 m, leaf N content, and N uptake by second-season corn after lime and gypsum application in a Rhodic Ferralsol under NT. Overall, the treatments consisted of a 4 × 4 factorial arrangement with four lime rates (0, 2.6, 5.4, and 8.1 Mg ha-1) and four gypsum rates (0, 4, 8, and 12 Mg ha-1). During the study period, second-season corn was cultivated for two years and fertilized with urea, for which the N losses through ammonia volatilization, soil pH, leaf N content, and N uptake values were quantified. The losses through ammonia volatilization were subjected to nonlinear regression using a logistic model, and the other variables were subjected to linear regressions. The lime applied by broadcasting on the soil surface in the NT increased the pH of the topsoil and increased N losses via NH3-N volatilization in the second-season corn. Further, the N losses in the NT treated with lime accounted for 58% of the applied N, which increased by 2.3 to 2.5% for each Mg ha-1 of lime applied. Therefore, lime or gypsum application did not improve the status of N in second-season corn in soils with low acidity and no S deficiency.

New approach for rapid estimation of leaf nitrogen, phosphorus, and potassium contents in apple-trees using Vis/NIR spectroscopy based on wavelength selection coupled with machine learning

Timely and rapid monitoring of apple trees nutrition status is vital for accurate management of nutrient fertilizers in order to improve the yield and quality, as well as to reduce the risk of environmental degradation. As a most frequently used method, tissue analysis used to assess the nutritional status of apple tree leaves is laborious, costly, time-consuming, environmentally unfriendly, and destructive. Ground-based sensors are able to efficiently provide information on nutritional status using leaf spectra reflectance. This research aims to establish a novel cost-effective and non-destructive approach for rapidly estimating the status of nitrogen (N), phosphorus (P), and potassium (K) in apple tree leaves based on Visible/Near-infrared (Vis/NIR) spectroscopy (500–1000 nm) coupled with machine learning. The Vis/NIR spectra of apple trees’ leaves were acquired. Then, leaf chemical contents of NPK elements were considered as reference points. Different pre-processing techniques were used to pre-treat the spectra. Four different chemometrics analysis consist of support vector machine (SVM), Artificial neural network (ANN), Random Forest (RF) and partial least square (PLS) were applied to predict NPK contents in comparison to actual values. In order to simplify the models, the sensitive wavelengths were extracted using three wavelength selection approaches, variable importance in projection (VIP), partial least squares (PLS), and random frog (Rfrog). The extracted feature wavelengths from PLS, VIP, and Rfrog methods were widely distributed in the visible and near-infrared regions of the spectrum. The performance of the developed models was tested using Residual Prediction Deviation (RPD) and the Ratio of Performance to Interquartile Distance (RPIQ). The results demonstrated that among all models, the non-linear modeling methods were superior to the linear model. The best results for estimation of Nitrogen (N), Phosphorus (P), and Potassium (K) elements were achieved by the models of MSC + D2-Rfrog-RF (rp = 0.985, RMSEP = 0.029%, RPD = 8.77, RPIQ = 7.72), SNV + D2-Rfrog-RF (rp = 0.977, RMSEP = 0.0053%, RPD = 6.42, RPIQ = 5.09) and SNV + D2-Rfrog-RF (rp = 0.978, RMSEP = 0.018%, RPD = 8.16, RPIQ = 7.01), respectively. The findings of the current approach may provide an efficient approach to predict in-situ NPK contents of apple trees based on leaf spectral reflectance.

Estimation of Winter Wheat Canopy Chlorophyll Content Based on Canopy Spectral Transformation and Machine Learning Method

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (Rv2) was 0.88, the RMSE of the validation set (RMSEv) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area.

Abomasal Nitrogen Flow Affects the Relationship between Dietary Nitrogen and Insulin-Like Growth Factor-I in Growing Lambs

Twelve abomasally cannulated wether lambs were fed isocaloric diets containing 9, 12 or 15% crude protein to determine insulin-like growth factor-I (IGF-I) responses to altered abomasal nitrogen flow and nitrogen status. Lambs were offered 1100 g/d of their respective diets. Voluntary feed consumption was not affected by nitrogen intake. Ruminal and total tract digestibilities of dry matter, organic matter and nitrogen increased linearly (P < 0.05) with increased dietary nitrogen. Abomasal flows of total, bacterial and rumen escape nitrogen increased (linear, P < 0.01), whereas dry matter and organic matter flows decreased (linear, P < 0.01). Total amino acid flow was greater (linear, P < 0.01) in lambs fed additional nitrogen due to increased (linear, P < 0.01) flows of essential and nonessential amino acids. Nitrogen retention and blood urea nitrogen increased linearly (P < 0.01). Serum IGF-I concentrations and relative hybridization intensity of hepatic IGF-I mRNA increased (linear, P < 0.05) as lambs consumed more nitrogen. Serum IGF-I and hepatic IGF-I mRNA were correlated positively (P < 0.05) with nitrogen intake and abomasal flows of nitrogen and various amino acids. These data provide evidence of a relationship between abomasal amino acid flow, as influenced by nitrogen intake, and hepatic gene expression and serum concentrations of IGF-I in growing lambs.

Comparison of statistical methods to fit critical nitrogen dilution curves

The critical nitrogen dilution curve (CNDC) is one of the most important models underpinning diagnostic tools of crop nitrogen (N) status. The CNDC relates plant N content (%N) to plant biomass under optimal nutritional conditions, using a simple nonlinear function with two parameters. More precisely, the CNDC estimates the critical %N (%NC) - the amount of %N needed to achieve maximum biomass at a certain moment. Thus, the comparison of a measured %N to %NC diagnoses if a plant is N deficient and if application of N fertilizer is necessary. Because of the practical importance of the CNDC, its parameters should be estimated as accurately as possible from field data. Two contrasting frameworks have been proposed in the literature for parameter estimation of the CNDC. The most popular approach is a sequential framework that consists of fitting two statistical models sequentially. Recently, an alternative framework has been introduced to estimate the parameters in a single step, using a hierarchical Bayesian model (hierarchical framework). In this study, we compare both methods and evaluate their performances for a large range of experimental designs. We consider datasets with different numbers of observation times (4, 8, 16), N fertilizer rates (3, 4, 5), and levels of measurement accuracy (low, medium, high). Our results show that the hierarchical framework outperforms the sequential framework under most scenarios. The sequential framework often resulted in poor statistical properties of the parameter estimates (e.g., increased bias and poorly calibrated confidence intervals) and to overestimated %NC values. The estimates for the amount of N needed to sustain N sufficiency levels were between 30 and 100 kg N ha−1 greater under the sequential framework, compared to the hierarchical framework. Thus, adopting the hierarchical framework could contribute to decreasing the risk of overfertilization and increasing the sustainability of agricultural systems.

Nitrogen nutrition diagnosis and estimation of seed cotton yield based on critical nitrogen concentration for drip irrigation cotton in Southern Xinjiang, China.

The stability and adaptability of the critical nitrogen concentration in drip-irrigated cotton fields in Southern Xinjiang were investigated by measuring the nitrogen uptake (Nuptake), nitrogen nutrition index (NNI), and accumulated nitrogen deficit (Nand). A two-year field study was conducted using five nitrogen levels (0, 75, 150, 300, and 450 kg·hm-2), with cotton cultivars 'Xinluzhong55' and 'Xinluzhong78' as the test materials. We analyzed the effects of nitrogen addition on plant dry matter (PDM) and critical nitrogen concentration dilution curves (PNCc). The Nuptake, NNI, and Nand models as well as a model of the relationships between NNI, Nand, and the relative seed cotton yield (RY) were established based on the PNCc for both cultivars. The results showed that the PNCc, maximum nitrogen concentration (PNCmax), and minimum nitrogen concentration (PNCmin) dilution models in drip-irrigated cotton showed power function relationships with plant dry matter (PDM), with determination coefficients (R2) of 0.905 and 0.960, 0.919 and 0.950, 0.934 and 0.969 for Xinluzhong55 and Xinluzhong78, respectively. Model validation with independent experimental data showed that the critical nitrogen concentrations in cotton grown under drip-irrigated conditions were simulated satisfactorily. The Xinluzhong55 and Xinluzhong78 1:1 line R2, root mean square error (RMSE) and relative estimation error (RE) of the simulated and observed critical nitrogen concentrations were 0.987, 0.051 g·kg-1, 1.95% and 0.984, 0.044 g·kg-1, 1.91%, respectively, showing good stability and adaptability between the two years. According to the Nuptake, NNI, and Nand model, we concluded that 300 kg·hm-2 was the optimum level of nitrogen application in drip-irrigated cotton in Southern Xinjiang. Based on the fitting results of nitrogen level and seed cotton yield, the theoretical appropriate nitrogen applications for Xinluzhong55 and Xinluzhong78 were 322.7 and 336.4 kg·hm-2, respectively. The R2 of NNI, Nand and RY in different growth stages were greater than 0.900, the NNI, Nand and RY estimation model of seed cotton yield based on 1:1 lines of R2, RMSE, and RE were 0.899-0.989, 0.05-0.13, and 4.1%-9.9% and 0.902-0.981, 0.04-0.12, and 5.1%-9.5%, all of which were highly significant. These results could provide new directions for the evaluation and diagnosis of optimum nitrogen application levels and nitrogen status, as well as the estimation of seed cotton yield in drip-irrigated cotton in Southern Xinjiang.

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R2=0.89, RMSE=0.68 g m–2, RE=14.62% for calibration and R2=0.83, RMSE=1.08 g m–2, RE=19.71% for validation) was much better than that at the all-leaf level (R2=0.73, RMSE=2.20 g m–2, RE=26.70% for calibration and R2=0.70, RMSE=2.48 g m–2, RE=31.49% for validation) and at the plant level (R2=0.66, RMSE=4.46 g m–2, RE=30.96% for calibration and R2=0.63, RMSE=3.69 g m–2, RE=24.81% for validation). Similarly, the XGB model (R2=0.65, RMSE=0.09, RE=8.59% for calibration and R2=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R2=0.62, RMSE=0.10, RE=7.92% for calibration and R2=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R2=0.64, RMSE=0.09, RE=9.24% for calibration and R2=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

Inorganic Fungicides (Phosphites) Instead of Organic Fungicides in Winter Wheat—Consequences for Nitrogen Fertilizer Productivity

Substitution of organic with inorganic fungicides (phosphites, Phi) does not change the efficiency of fertilizer nitrogen (Nf) in winter wheat. This hypothesis was tested in the 2016/2017 and 2017/2018 growing seasons. A two-factorial experiment with three phosphite variants (Cu–Phi, Mg–Phi, and Cu/Mg) and six plant protection methods (fungicides + Phi ⟶ reduced fungicide frequency + phosphite ⟶ phosphite). Grain yield decreased with increasing frequency of phosphites instead of fungicides. The decrease in yields was 3.6 t ha−1 in the favorable 2016/2017 and 1.1 t ha−1 in the dry 2017/2018. The primary reason for yield decrease in a given growing season was increased wheat infestation by pathogens. The direct cause was disturbances in the nitrogen status of wheat after flowering on treatments with a predominance of phosphites. The thousand grain weight (TGW) responded negatively to reduced fungicide application frequency. The critical stage in the assessment of pathogen pressure on wheat was the medium milk phase (BBCH 75). At this stage, indices of SPAD and leaf greenness together with indices of wheat infestation with pathogens allowed for a reliable prediction of both TGW and grain yield. It can be concluded that phosphites do not substitute organic fungicides in limiting pathogen pressure in winter wheat. Moreover, increased pressure of pathogens significantly reduces Nf productivity.

The Functional Profile and Antioxidant Capacity of Tomato Fruits Are Modulated by the Interaction between Microbial Biostimulants, Soil Properties, and Soil Nitrogen Status.

The application of microbial biostimulants to plants has revealed positive effects related to nutrients uptake, stress tolerance, root development and phenological growth. However, little information is available exploiting the potential synergistic biostimulant action of microbes on the functional quality of the yields. The current research elucidated the effect of single or coupled action of biostimulants, associated with either optimal or reduced nitrogen application, on the functional quality of tomato fruits. Chemical assays and untargeted metabolomics were applied to investigate Rhizoglomus irregulare and Funneliformis mosseae administration (both being arbuscular mycorrhiza, AMF), under optimal or low N input conditions, alone or coupled to Trichoderma atroviride application. The coupling of AMF and Trichoderma fungal inoculations resulted in a synergistic biostimulant effect on tomato fruits under sub-optimal fertility, revealing improved concentrations of carotenoid compounds-B-carotene (0.647 ± 0.243 mg/100 g), Z-carotene (0.021 ± 0.021 mg/100 g), 13-z-lycopene (0.145 ± 0.052 mg/100 g) and all-trans-lycopene (12.586 ± 1.511 mg/100 g), and increased values for total phenolic content (12.9 ± 2.9 mgGAE/g), total antioxidant activity (phosphomolybdenum, 0.9 ± 0.2 mmolTE/g), radical scavenging activity (DPPH, 3.4 ± 3.7 mgTE/g), reducing power (FRAP, 23.6 ± 6.3 mgTE/g and CUPRAC, 37.4 ± 7.6 mg TE/g), and enzyme inhibitory activity (AChE, 2.4 ± 0.1 mg GALAE/g), when compared to control. However, evidence of carotenoid and bioactive compounds were exclusively observed under the sub-optimal fertility and no significant differences could be observed between the biostimulant treatment and control under optimal fertility.

Identification of photosynthetic parameters for superior yield of two super hybrid rice varieties: A cross-scale study from leaf to canopy.

Enhancing photosynthetic capacity is widely accepted as critical to advancing crop yield. Therefore, identifying photosynthetic parameters positively related to biomass accumulation in elite cultivars is the major focus of current rice research. In this work, we assessed leaf photosynthetic performance, canopy photosynthesis, and yield attributes of super hybrid rice cultivars Y-liangyou 3218 (YLY3218) and Y-liangyou 5867 (YLY5867) at tillering stage and flowering stage, using inbred super rice cultivars Zhendao11(ZD11) and Nanjing 9108 (NJ9108) as control. A diurnal canopy photosynthesis model was applied to estimate the influence of key environmental factors, canopy attributes, and canopy nitrogen status on daily aboveground biomass increment (AMDAY). Results showed that primarily the light-saturated photosynthetic rate at tillering stage contributed to the advancing yield and biomass of super hybrid rice in comparison to inbred super rice, and the light-saturated photosynthetic rate between them was similar at flowering stage. At tillering stage, the higher CO2 diffusion capacity, together with higher biochemical capacity (i.e., maximum carboxylation rate of Rubisco, maximum electron transport rate (J max), and triose phosphate utilization rate) favored leaf photosynthesis of super hybrid rice. Similarly, AMDAY in super hybrid rice was higher than inbred super rice at tillering stage, and comparable at flowering stage partially due to increased canopy nitrogen concentration (SLNave) of inbred super rice. At tillering stage, model simulation revealed that replacement of J max and g m in inbred super rice by super hybrid rice always had a positive effect on AMDAY, and the averaged AMDAY increment was 5.7% and 3.4%, respectively. Simultaneously, the 20% enhancement of total canopy nitrogen concentration through the improvement of SLNave (TNC-SLNave) resulted in the highest AMDAY across cultivars, with an average increase of 11.2%. In conclusion, the advancing yield performance of YLY3218 and YLY5867 was due to the higher J max and g m at tillering stage, and TCN-SLNave is a promising target for future super rice breeding programs.

Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.