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Few studies have assessed the common, yet unproven, hypothesis that an increase of plant nitrogen (N) uptake and/or recovery efficiency (NRE) will reduce nitrous oxide (N O) emission during crop production. Understanding the relationships between N O emissions and crop N uptake and use efficiency parameters can help inform crop N management recommendations for both efficiency and environmental goals. Analyses were conducted to determine which of several commonly used crop N uptake-derived parameters related most strongly to growing season N O emissions under varying N management practices in North American maize systems. Nitrogen uptake-derived variables included total aboveground N uptake (TNU), grain N uptake (GNU), N recovery efficiency (NRE), net N balance (NNB) in relation to GNU [NNB ] and TNU [NNB ], and surplus N (SN). The relationship between N O and N application rate was sigmoidal with relatively small emissions for N rates <130 kg ha , and a sharp increase for N rates from 130 to 220 kg ha ; on average, N O increased linearly by about 5 g N per kg of N applied for rates up to 220 kg ha . Fairly strong and significant negative relationships existed between N O and NRE when management focused on N application rate ( = 0.52) or rate and timing combinations ( = 0.65). For every percentage point increase, N O decreased by 13 g N ha in response to N rates, and by 20 g N ha for NRE changes in response to rate-by-timing treatments. However, more consistent positive relationships ( = 0.73-0.77) existed between N O and NNB , NNB , and SN, regardless of rate and timing of N application; on average N O emission increased by about 5, 7, and 8 g N, respectively, per kg increase of NNB , NNB , and SN. Neither N source nor placement influenced the relationship between N O and NRE. Overall, our analysis indicated that a careful selection of appropriate N rate applied at the right time can both increase NRE and reduce N O. However, N O reduction benefits of optimum N rate-by-timing practices were achieved most consistently with management systems that reduced NNB through an increase of grain N removal or total plant N uptake relative to the total fertilizer N applied to maize. Future research assessing crop or N management effects on N O should include N uptake parameter measurements to better understand N O emission relationships to plant NRE and N uptake.

Nitrogen deficiency results in the yellowing of old leaves and premature senescence, ultimately leading to crop yield limitations. In this study, we overexpressed phospho enol pyruvate carboxykinase 2 ( PCK2 ) from maize in rice (OE) and observed that OE outperformed the wild type under low nitrogen stress conditions, with relatively higher biomass and photosynthetic pigment contents compared with the wild type. Additionally, OE enhances nitrogen absorption and assimilation. Therefore, the photosynthesis is enhanced and the sensitivity to changes in carbon-nitrogen balance is reduced compared with the wide type. Overall, the over-expression of ZmPCK2 in rice could enhance its tolerance to low nitrogen stress.

Nitrogen is essential for rice growth and yield formation, but traditional methods for assessing nitrogen status are often labor-intensive and unreliable at high nitrogen levels due to saturation effects. This study evaluates the effectiveness of flavonoid content (Flav) and the Nitrogen Balance Index (NBI), measured using a Dualex sensor and combined with machine learning models, for precise nitrogen status estimation in rice. Field experiments involving 15 rice varieties under varying nitrogen application levels collected Dualex measurements of chlorophyll (Chl), Flav, and NBI from the top five leaves at key growth stages. Incremental analysis was performed to quantify saturation effects, revealing that chlorophyll measurements saturated at high nitrogen levels, limiting their reliability. In contrast, Flav and NBI remained sensitive across all nitrogen levels, accurately reflecting nitrogen status. Machine learning models, particularly random forest and extreme gradient boosting, achieved high prediction accuracy for leaf and plant nitrogen concentrations (R 2 > 0.82), with SHAP analysis identifying NBI and Flav from the top two leaves as the most influential predictors. By combining Flav and NBI measurements with machine learning, this approach effectively overcomes chlorophyll-based saturation limitations, enabling precise nitrogen estimation across diverse conditions and offering practical solutions for improved nitrogen management in rice cultivation.

Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat.

The Nitrogen Balance Index is a key indicator of crop nitrogen status, but conventional monitoring methods are invasive, costly, and unsuitable for large-scale application. This study targets early-season winter wheat in the Guanzhong Plain and proposes a framework that integrates Sentinel-2 imagery with Sen2Res super-resolution reconstruction, multi-feature optimization, and interpretable machine learning. Super-resolved imagery demonstrated improved spatial detail and enhanced correlations between reflectance, texture, and vegetation indices and the Nitrogen Balance Index compared to native imagery. A two-stage feature-selection strategy, combining correlation analysis and recursive feature elimination, identified a compact set of key variables. Among the tested algorithms, the random forest model achieved the highest accuracy, with R2 = 0.77 and RMSE = 1.57, representing an improvement of about 20% over linear models. Shapley Additive Explanations revealed that red-edge and near-infrared features accounted for up to 75% of predictive contributions, highlighting their physiological relevance to nitrogen metabolism. Overall, this study contributes to the remote sensing of crop nitrogen status through three aspects: (1) integration of super-resolution with feature fusion to overcome coarse spatial resolution, (2) adoption of a two-stage feature optimization strategy to reduce redundancy, and (3) incorporation of interpretable modeling to improve transparency. The proposed framework supports regional-scale NBI monitoring and provides a scientific basis for precision fertilization.

Abstract Nitrogen is an essential plant nutrient, but in excess amounts in the soil can cause significant water quality problems. Since nitrate is very soluble and is not retained by soil, it easily leaches into groundwater and contaminates it. Therefore, modeling nitrate concentration derived from N-fertilizer in the area where the land dominating crop coverage helps to ensure the security of soil and water and also the environmental sustainability actions in the agricultural watershed. Adding N-fertilizer without understanding the concentration of nitrates in the soil and neglecting responsibilities and lacking concern of excess application of nitrogen N fertilizers on agriculture can cause further problems on groundwater resources. In order to evaluate nitrate contamination on surface water and groundwater, estimating partial nitrogen balance (PNB) in crop land is essential. The objective of this study was to model the crop land partial nitrogen balance (PNB) for pre-assessment of nitrate contamination in the downstream of Bilate watershed crop land based on agricultural field N-fertilizer application loss in a scenario within Bilate watershed. The loss of agricultural nitrogen fertilizer in the Bilate watershed is one of the major factors that may contribute to nitrate contamination in the downstream water bodies in Bilate watershed. Geographically weighted regression (GWR) model with (EO-MODIS 250 m-NDVI) and time series cropland from (MODIS-MCD12Q1-IGBP of crop land class) has been utilized. Field crop data using GPS is collected from the upper, middle, and lower parts of watershed confirming the IGBP crop land classification (0.92 kappas) has scored. Additionally, the (MODIS 250 m–NDVI) data observation has been calibrated by the Google Earth Engine using a machine learning approach. Based on the FAO-Agricultural Stress Index System (ASIS), crop growth phonology curves value interval has been indexed, and simulated crop growth index has been validated for 0.25 min and 0.75 max crop growth curves. The results were utilized to replicate the time series heterogeneous crop pattern on crop land NDVI mean zonal statics. For last 20 years, a partial nitrogen balance for observed nitrogen application (Nkg/ha/year−1) and crop N uptake (Nkg/ha/year−1) is predicted. For the simulated outcome, the model has been verified for its linear correlation value of (R2 of 0.9986). The idea underlying this research is based on the scientific fact that nitrogen (NO3−-N) contamination of surface and subsurface water is unattainable without knowledge of the nitrogen level above the root zone to the particular crop zonal area.

Dietary fibre concentration and particle size influence chewing behaviour, ruminal fermentation and digesta passage in dairy cows and through this, may impact nitrogen (N) use and excretion by the animals. The aim was thus to evaluate the effects of physically effective neutral detergent fibre (peNDF) concentration on chewing behaviour, ruminal fermentation, passage rate and protein metabolism in four lactating, rumen-cannulated Holstein cows in a 4 × 4 latin square design. Four total mixed rations with identical ingredients, chemical composition and a negative ruminal N balance (–2.1 g/kg dry matter) were tested. They varied in peNDF concentration, adjusted by feed mixing time: low (L), medium-low (ML), medium-high (MH) and high (H) peNDF. Nutrient intakes, number of total chews, organic matter digestibility and yield and efficiency of ruminal microbial protein synthesis responded quadratically to increasing peNDF concentration, with greater values for MH and ML diets. While rumination and total chewing intensity (min/kg dry matter) increased with increasing peNDF concentration, milk yield and composition, digesta passage rates and concentrations of ammonium-N and volatile fatty acids in rumen fluid were similar across diets. Energy-corrected milk yield and partitioning of N between milk and urine responded quadratically to increased peNDF concentration. Energy-corrected milk yield and the percentage of ingested N secreted via milk were lower, but the percentage of N intake excreted via urine was greater for MH and ML diets. Hence, feeding dairy cows a low-protein diet with varying peNDF concentrations affects their chewing behaviour, nutrient digestion and protein metabolism.

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.

This study evaluated the effect of sex on growth performance, carcass characteristics, meat quality, and blood metabolite parameters of Awassi lambs. Twenty-seven Awassi lambs were allocated into two sex groups: males (n=13) and females (n=14). Lambs were individually penned and fed according to the nutritional needs of small ruminants. The experimental period of the study continued for 63 days, preceded by seven days for dietary and pen adaptation. On day 56 of the experimental period, five lambs of each sex group were randomly chosen and distributed in metabolism chambers to examine the digestibility and nitrogen balance. After 70 days of the trial, all lambs were butchered to determine their carcasses and meat quality. The average dry matter (DM) and crude protein (CP) intakes were significantly affected by sex (p < 0.05). Both sexes had similar DM and CP digestibility. Male lambs had greater nitrogen intake and retention (p < 0.05). Growth performance, weights of fasting animals, weights of hot and cold carcasses, non-carcass parts and carcass cuts were influenced (p < 0.05) by the lambs, sex.

One of the most important factors influencing productivity in the dairy goat farming is the balanced animal feeding. The composition and structure of the diet influence not only the level of milk production in dairy goats, but also its functional properties. It is important to search for inexpensive feed products of plant origin that have high nutritional value and contain the biologically active substances with antioxidant, antimicrobial and antiinflammatory effects. The introduction of by-products of agro-industrial production into the diets of farm animals allows us to expand the range of domestic inexpensive feed resources. Coriander cake can be considered as a feed product that meets all the above requirements, and therefore its use can serve as an effective alternative for providing goats with nutrients. An assessment was made of the digestibility of dietary nutrients, nitrogen balance and productivity in Saanen goats when different levels of coriander cake were introduced into the diet as a functional feed. It has been established that coriander cake can be used as an alternative feed with specified functional characteristics.