Search Results (11)

Paeonia ostii, a significant perennial woody oil crop in China, is notable for its seeds’ high oil content and elevated levels of unsaturated fatty acids. However, there is currently a lack of scientific fertilisation protocols and targeted nutrient management for P. ostii. The concentrations of macronutrients (N, P, K, Ca, and Mg) and micronutrients (Fe, Mn, Zn, and Cu) were determined in the leaves at five distinct growth stages: flowering, initial fruit set, fruit expansion, late fruiting, and foliar senescence. The levels of N and P were found to be at their highest point during the flowering stage, after which they declined significantly. In contrast, the levels of K remained relatively stable throughout the growth phase, while Mg levels increased significantly to peak at fruit expansion. The level of Ca increased, reaching its peak at the late fruiting stage. The annual average content of micronutrients in P. ostii leaves was as follows: Fe > Mn > Zn > Cu. Furthermore, it was observed that the concentrations of Fe and Mn oscillated, while the concentration of Cu decreased significantly after flowering. Additionally, Zn concentrations remained stable throughout the various stages. Multivariate analyses, including PCA, nutrient ratio analysis, and an integrated nutrient stability index, further revealed coordinated shifts in leaf nutrient composition and indicated that May and June were relatively stable periods for nutrient assessment. Considering both the nutrient stability and the phenological relevance, June, corresponding to the fruit expansion stage, was considered a practical sampling window for foliar nutrient diagnosis. These findings contribute to the definition of an appropriate sampling window for foliar nutrient diagnosis, thereby providing a useful basis for nutrient monitoring and future fertilisation studies in P. ostii. Full article

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main score used by different approaches, like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis, to study nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis’s early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars’ potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores, including the grain yield (kg ha⁻¹) and the nutrient composition (mg kg⁻¹) of different corn varieties, is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A ‘nutrient signature’ model is proposed based on the impact of the environmental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low- and high-yielding subpopulations is established at 12,000 kg ha⁻¹, and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and validated by the Cate–Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model’s predictive accuracy for the GNII is significantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions. Full article

Fresh sweet and waxy maize (Zea mays) are valuable specialty crops in southern China. Hyperspectral remote sensing offers a powerful tool for detecting maize foliar nutrients non-destructively. This study aims to investigate the capability of leaf spectroscopy (SVC HR-1024i spectrometer, wavelength range: 400–2500 nm) to retrieve maize foliar nutrients. Specifically, we (1) explored the effects of nitrogen application rates (0, 150, 225, 300, and 450 kg·N·ha⁻¹), maize cultivars (GLT-27 and TGN-932), and growth stages (third leaf (vegetation V3), stem elongation stage (vegetation V6), silking stage (reproductive R2), and milk stage (reproductive R3)) on foliar nutrients (nitrogen, phosphorus, and carbon) and leaf spectra; (2) evaluated the transferability of the regression and physical models in retrieving foliar nutrients across maize cultivars. We found that the PLSR (partial least squares regression), SVR (support vector machine regression), and RFR (random forest regression) regression model accuracies were fair within a specific cultivar, with the highest R² of 0.60 and the lowest NRMSE (normalized RMSE = RMSE/(Max − Min)) of 17% for nitrogen, R² of 0.19 and NRMSE of 21% for phosphorous, and R² of 0.45 and NRMSE of 19% for carbon. However, when these cultivar-specific models were used to predict foliar nitrogen across cultivars, lower R² and higher NRMSE values were observed. For the physical model, which does not rely on the dataset, the R² and NRMSE for foliar chlorophyll-a and -b (C_ab), carotenoid (C_xc), and equivalent water thickness (EWT) were 0.76 and 15%, 0.67 and 34%, and 0.47 and 21%, respectively. However, the prediction accuracy for foliar nitrogen, expressed as foliar protein in PROSPECT-PRO, was lower, with an R² of 0.22 and NRMSE of 27%, which was comparable to that of the regression models. The primary reasons for this limited transferability were attributed to (1) the insufficient number of samples and (2) the lack of strong absorption features for foliar nutrients within the 400–2500 nm wavelength range and the confounding effects of other foliar biochemicals with strong absorption features. Future efforts are needed to investigate the physical mechanisms underlying hyperspectral remote sensing of foliar nutrients and incorporate transfer learning techniques into foliar nutrient models. Full article

Citrus trees require a balanced and adequate supply of macronutrient and micronutrient elements for high yield and fruit quality. Foliar nutrient analysis has been widely used in fruit-tree nutrient diagnosis and fertilization calculation. However, there is no information on ways to produce optimal fruit quality in sweet oranges. In the present study, fruit and leaf samples were collected from 120 ‘Bingtang’ sweet orange [Citrus sinensis (L.) Osbeck] orchards during four consecutive years (2019–2022). Parameters of leaf nutrition and fruit quality were analyzed based on these samples. Diagnostic results based on leaf classification standards indicated that the most deficient elements were Ca, Mg, and B, followed by N and Zn. Fruit quality, determined by single fruit weight (SFW), fruit shape index (FSI), total soluble solids (TSS), titratable acidity (TA), vitamin C (Vc), and maturation index (MI = TSS/TA) during fruit maturation, exhibited inconsistent responses to leaf mineral nutrition concentrations. The leaf-nutrient optimum values for high quality of the ‘Bingtang’ sweet orange fruit were ranges of 2.41–4.92% N, 0.10–0.28% P, 1.30–2.11% K, 2.99% Ca, 0.26–0.41% Mg, 340–640 mg/kg S, 89.65–127.46 mg/kg Fe, 13.48–51.93 mg/kg Mn, 2.60–13.84 mg/kg Cu, 15.59–51.48 mg/kg Zn, and 53.95 mg/kg for B. These results suggest the leaf-nutrient optimum values for diagnosis can be used not only to identify the nutrient constraints but also to provide guidance for the establishment of fertilization regimes in citrus cultivation. Full article

Yerba mate is a forest species of both cultural and economic importance growing in the subtropical regions of South America, especially in the south of Brazil. Despite its importance, yerba mate has never received enough attention from researchers, so the nutritional sufficiency ranges and critical levels have not yet been determined. This research aimed to establish these parameters for yerba mate to enable its foliar diagnosis. A total of 167 leaf samples were collected from production fields located in the five yerba mate-growing regions in Rio Grande do Sul, and the leaf nutrients were determined by standard chemical methods. The yield of each production field was accessed, and the cutoff value separating low- and high-yield groups was calculated in 16.75 Mg ha⁻¹. The multivariate compositional nutrient diagnosis (CND) standards were determined, and nutrient interactions were estimated by correlation and principal component analyses. There was no positive correlation between any single nutrient and yield, even in the high-yield population, evidencing that a higher yield is the outcome of the balance among all nutrients. Excess of B occurred in one-third of the low-yield samples, while deficiency of Cu and K occurred in one-fourth of these samples. Finally, we established the adequate leaf nutrient levels for yerba mate. Full article

This study was conducted to evaluate fertilization management based on fruit nutrient removal, soil nutrient-supplying capacity and tree nutritional status with standard nutrient values as a reference and the effects on fruit size and yield in Hass avocado trees. The soil chemical characteristics, foliar nutrient content interpreted with the Kenworthy balance index (KBI) method and fruit nutrient removal for a planned yield of 20 ton/ha were used to determine the fertilization management plan for the crop. The experimental area had soils with Andic characteristics and sandy loam texture, low cation exchange capacity and acidic pH. The farmer’s standard fertilization plan was based on excessive fertilizer doses for N, P, K and Ca, and an imbalance of P, Ca and micronutrients was observed with the diagnosis of plant nutrient status. The fertilizer plan based on the KBI method had an effect on yield variables in the second crop year, with an increase in production of 20 kg/tree as well as an increase in the percentage of fruits with a size higher than 22 (165–196 g/fruit) according to the Codex Alimentarius standards. These findings indicate that the reincorporation of minerals extracted by the harvest into the soil and the plant nutrient status are useful tools to guide crop fertilization management when fine-tuned to local soil chemical conditions and crop requirements to minimize nutrient losses. Full article

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R²). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops. Full article

Nutrient diagnosis of orange (Citrus sinensis) groves in Brazil relies on regional information from a limited number of studies transferred to other environments under the ceteris paribus assumption. Interpretation methods are based on crude nutrient compositions that are intrinsically biased by genetics X environment interactions. Our objective was to develop accurate and unbiased nutrient diagnosis of orange groves combining machine learning (ML) and compositional methods. Fruit yield and foliar nutrients were quantified in 551 rainfed 7–15-year-old orange groves of ‘Hamlin’, ‘Valência’, and ‘Pêra’ in the state of São Paulo, Brazil. The data set was further documented using soil classification, soil tests, and meteorological indices. Tissue compositions were log-ratio transformed to account for nutrient interactions. Ionomes differed among scions. Regression ML models showed evidence of overfitting. Binary ML classification models showed acceptable values of areas under the curve (>0.7). Regional standards delineating the multivariate elliptical hyperspace depended on the yield cutoff. A shapeless blob hyperspace was delineated using the k-nearest successful neighbors that showed comparable features and reported realistic yield goals. Regionally derived and site-specific reference compositions may lead to differential interpretation. Large-size and diversified data sets must be collected to inform ML models along the learning curve, tackle model overfitting, and evaluate the merit of blob-scale diagnosis. Full article

To ensure good fertilization, it is necessary to know the optimum nutrient levels for each crop. The most common method for obtaining this information for almond trees is to perform a foliar analysis coupled with the use of interpretive tools such as the traditional range of normality. However, currently, there are other, more sophisticated methods such as the DRIS (Diagnosis and Recommendation Integrated System) and the CND (Compositional Nutrient Diagnosis) which take into account the relationship between nutrients. However, little information is available with respect to these methods in the case of almond trees. In the present work, 288 samples of three contrasting varieties of almond were analyzed—Ferraduel, Ferragnes, and Garrigues (Prunus dulcis, Mill.)—corresponding to bi-weekly sampling between the months of May and September. Leaf analysis data, run with different mathematical and statistical models, lead to knowledge of the optimum period for harvesting samples and the determination of the ranges of normality and norms of DRIS and CND for the Ferraduel, Ferragnes, and Garrigues varieties. Data gained from the leaf nutrient content reported that the best season to harvest and interpret leaf samples was July. In addition, Ferraduel and Ferragnes had higher N, P, and K (2.22, 0.14, and 1.04 mg Kg⁻¹ dw, respectively) than Garrigues (2.00, 0.09. 0.67 mg Kg⁻¹ dw). The norms obtained with the leaf mineral data showed similar values between the Ferraduel and Ferragnes varieties but different values for Garrigues variety. Therefore, Garriges had the highest N/P, N/K, P/K, and P × Mg norms in the DRIS method and the highest V_N and V_Ca norms in the CND method. Full article

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha⁻¹. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders. Full article

Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm⁻¹ and the global nutrient imbalance index (CNDr²) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for I_N, (−0.44, +0.44) for I_P, (−0.63, +0.63) for I_K, (−0.94, +0.94) for I_Ca, (−1.05, +1.05) for I_Mg, (−0.80, +0.80) for I_Fe, (−0.74, +0.74) for I_Cu, (−0.80, +0.80) for I_B, (−0.93, +0.93) for I_Zn, (−1.04, +1.04) for I_Mn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases. Full article