Crop Samples Research Articles

Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery

Pasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g., soil and crop sampling). This study utilized high-resolution satellite and unmanned aerial system (UAS) imagery to evaluate vegetation characteristics of a pasture field location with two grazing densities (low and high, applied in the years 2015–2019) and four fertility treatments (control, manure, mineral, and compost tea, applied annually in the years 2015–2019). The pasture productivity was assessed through satellite imagery annually from the years 2017 to 2019. The relation and variation within and between the years were evaluated using vegetation indices extracted from satellite and UAS imagery. The data from the two sensing systems (satellite and UAS) demonstrated that grazing density showed a significant effect (p < 0.05) on pasture crop status in 2019. Furthermore, the mean vegetation index data extracted from satellite and UAS imagery (2019) had a high correlation (r ≥ 0.78, p < 0.001). These results show the potential of utilizing satellite and UAS imagery for crop productivity assessment applications in small to medium pasture research and management.

Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions

Site-specific weed management in Precision Agriculture is becoming a popular topic among researchers and farmers. The objective of this study was to classify weeds and crop species using RGB image texture features with the comparison of the Support Vector Machine (SVM) classification model and deep learning-based visual group geometry 16 (VGG16) classification models. A total of 3792 RGB images of crop and weed samples were captured from the greenhouse, including 2271 weed images and 1521 crop images. ReliefF feature selection algorithm was applied to select the most important features for prediction models. The SVM and VGG16 deep learning classifiers were used to classify four weeds (horseweed, kochia, ragweed, and waterhemp) and six crop species (black bean, canola, corn, flax, soybean, and sugar beets). Accuracy, f1-score, and kappa score metrics were used to evaluate model performance and data reliability. The VGG16 model classifiers had outperformed all the SVM model classifiers. The results showed that average f1-scores of the VGG16 model classifier were obtained between 93% and 97.5%. The f1-score value of 100% was obtained for the corn class in the VGG16 Weeds-Corn classifier, which seems outstanding for the corn crop production system. This study shows promising results of using a deep learning algorithm (VGG16) for weed identification in site-specific weed management in precision agriculture. • Machine learning (ML) and deep learning (DL) based classifiers were built for six types of crop production systems. • Local binary pattern and Gray Level Co-Occurrence Matrix texture features were extracted for ML. • ReliefF feature selection algorithm was used for machine learning feature engineering. • VGG16 classifiers outperformed the SVM classifiers with an f1-score value of over the 93%. • The highest f1-score value of 100% was obtained for the corn class in the Weeds-Corn VGG16 classifier.

Dynamic changes in protein concentrations of keratins in crop milk and related gene expression in pigeon crops during different incubation and chick rearing stages

ABSTRACT 1. The objective of this study was to examine the keratin composition of crop milk, the variation of epithelial thickness and keratin (K) gene expression in samples from young pigeon during incubation and chick rearing. 2. Crop milk was collected from 1-, 3- and 5-day-old squab crops for keratin content analysis. Results showed that K4 accounted for the highest proportion of all detected keratins. 3. In total, 42 pairs of adult pigeons were allocated to seven groups according to different stages to collect crop samples. Gene expression studies showed that the K3 gene expression was maximised at rearing Day 15 (15) and R1 in males and females, respectively. K6a gene level was the greatest at R15 in females, whereas it peaked at incubation Day 4 (I4) in males. The K12, K13, K23 and K80 gene levels were inhibited at the peak period of crop milk formation in comparison with I4. In females, K cochleal expression peaked at I10, whereas it was the greatest at R25 in males. K4 and K14 gene expression was the highest at I10 in females, while K4 and K14 were minimised at I17 and R7 in males, respectively. Gene expressions of K5, K8, K19 and K20 in males and K19 in females were maximised at R1. The K5, K20 and K75 gene levels in females peaked at R7. K75 and K8 expressions in males and females reached a maximum value at R25 and I17, respectively. 4. The epithelial thickness of male and female crops reached their greatest levels at R1 and had the highest correlation with K19. 5. These results emphasised the importance of keratinisation in crop milk formation, and different keratins probably play various roles during this period. The K19 was probably a marker for pigeon crop epithelium development. The sex of the parent pigeon affected keratin gene expression profiles.

Comparison of grain cadmium and arsenic concentration between main and ratoon crop in rice ratooning system

Rice ratooning system is becoming increasingly important for food security in China, however, information on grain cadmium (Cd) and arsenic (As) levels is lacking. We collected grain samples of main crop (MC) and ratoon crop (RC) from five sites, where the same eleven varieties were planted, and determined the Cd and As concentration in brown rice. Results showed that differences in grain Cd level between MC and RC were inconsistent across experimental sites, although the average value was comparable. In contrast, the grain As level was significantly higher in MC than in RC by 99.8% on average, which was consistent across all sites. Moreover, there was a significant positive correlation in grain Cd concentration between RC and MC. Overall, RC-produced rice is safer than MC with dramatically lower As concentration, and planting rice varieties with low Cd accumulation capacity is important for production of safe rice in rice ratooning system.

Pilot study on the effect of secondary copper smelters on polychlorinated naphthalene contamination in surrounding agricultural areas

Polychlorinated naphthalenes (PCNs) are dioxin-like persistent organic pollutants that are primarily produced unintentionally during industrial thermal processes. These compounds are harmful to the environment and human health. This study investigated the occurrences of all 75 PCN congeners in agricultural areas near secondary copper smelters in China. The PCN concentrations in aquatic foods, eggs, crops, sediments and soils within 10 km of these smelters were higher than those in samples collected 20–30 km away from such facilities. In contrast, the PCN concentrations in compound animal feed samples collected at different distances from the secondary copper smelters were comparable to one another. Similar PCN homologue patterns were found in crop, egg, feed and soil samples collected at different distances from the smelters but the homologue profiles of PCNs in aquatic food and sediment samples collected from different distances varied. Lower chlorinated naphthalenes were the predominant homologues in most samples. The contamination of farm animals and crops with PCNs might result from the emission of these compounds from such sources into the surrounding environment. However, the health risks associated with human exposure to PCNs through food consumption are low for both distances (<10 km and 20–30 km) from secondary copper smelters.

Appraisal of environmental, ecological and carcinogenic risk due to heavy metals in a sewage and solid waste contaminated area

ABSTRACT Unsystematic use of city waste, sludge and sewage water in agricultural fields elevated the heavy metal load in soil warranting critical introspection. The present study aims to delineate heavy metals in polluted soils of peri-urban Kolkata, determine pollution and ecological risk, assess the heavy metal uptake and human dietary risk in commonly cultivated crops. Total 36 sampling points were selected and paired sampling of soil and crops were done. Significant available heavy metal (mg kg-1) in surface soil like Cd (0.72-3.76), Pb (28.59-87.29), Ni (3.11-8.73), and Cr (2.23-8.43) and in crops as Cd (0.55-2.73), Pb (2.82-29.85), Ni (2.45-8.27) and Cr (0.80-6.85) was observed. Use of machine learning revealed soil pH and organic carbon as the influencing factors of heavy metal contamination. Moderate to considerable environmental and ecological risk was observed. Substantial carcinogenic risk was observed as Cd (1.13x10-3 to 3.77x10-4), Pb (2.75x10-4 to 3.47x10-5), Ni (1.15x10-3 to 1.73x10-4), and Cr (2.78x10-4 to 1.18x10-4), for almost all crops, with a general trend of spinach > cabbage > radish > red amaranthus > bottle gourd > maize. The results can serve as the baseline information to enforce policy decisions for solving this conundrum of managing waste and saving human lives.

Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas.

Growth, yield and microbial contamination of lettuce (Lactuca sativa L.) grown under two types of cultivation system as influenced by different organic fertilizers

Increasing popularity of lettuce in the tropics is becoming evident due to its widespread use in health and nutrition. Lettuce, a ready-to-eat vegetable is expected to be safe and of good quality. A study was conducted to assess the effect of different organic fertilizers on the growth, yield and microbial contamination of lettuce grown under two types of cultivation system. This was carried out in a combined analysis arranged in randomized complete block design with the following treatments: without organic fertilizer (control), cow manure, fresh chicken dung, dried chicken dung, vermicast and goat manure. Results showed that lettuce grown under structure performed better than in open field as manifested by early heading, bigger polar head size, longer and broader leaves and more compact head. Among the organic fertilizers used, dried chicken dung and vermicast enhanced the yield and yield components of lettuce. Microbial contaminants like E. coli were detected in soils applied with organic fertilizers. The highest microbial count was recorded in fresh chicken dung-treated soils in both open field and under structure. E. coli contamination in lettuce leaves was more evident in the open field than under structure. Regardless of soil and crop samples, treated or not, positive detection of Salmonella was observed.

Assessment of chromium toxicity and potential health implications of agriculturally diversely irrigated food crops in the semi-arid regions of South Asia

Heavy metal deposition in crops irrigated with wastewater is a serious environmental issue in many developing countries. This research looked at the probable health impacts of chromium (Cr) in water, soil, and food crop samples. The concentration of Cr was measured in samples taken from three sites: Sargodha City, Sahiwal, and Shahpur. Chromium levels were found to vary from 0.011 to 0.067 mg/L in water, to 0.223–2.49 in soil, and from 0.17 to 1.74 mg/kg in crops, respectively. Cr levels in the water, soil, and crops met World Health Organization requirements. Crop samples showed a positive correlation with soil chromium concentrations. There was a wide range in the bioconcentration factor (BCF), pollution load index (PLI), enrichment factor (EF), daily metal intake (DIM), health risk index (HRI), and target hazard quotient (THQ), which were all between 0.16 and 1.79. The BCF and EF were greater than one, which proves a high level of Cr mobility and metal enrichment. The PLI, DIM, HRI, and THQ of metal values were less than one, indicating that toxic metals were present in lower quantities in food crops and had no carcinogenic effects on consumers. Consistent monitoring of water quality, crops, soil, and better agricultural practices that inhibit metals from entering agricultural food reduce the potential health risks to consumers.

Genetic Diversity Analysis based on the Virulence, Physiology and Regional Variability in Different Isolates of Powdery Mildew in Pea.

Powdery mildew is an omnipresent disease that reduces the yield and quality of pea crops (Pisum sativum L.). To examine the powdery mildew pathogen’s morphological, molecular, and genetic diversity, we collected samples of powdery mildew-affected pea crops from ten distinct locations in the Nilgiris district of Tamil Nadu, India. The pathogen Erysiphe pisi was identified morphologically based on anamorphic characters. Molecular identification of E. pisi isolates was befitted by targeting the internal transcribed spacer (ITS) region of rDNA and specific primers of powdery mildew fungi. The genetic variation between ten different E. pisi isolates collected from topographically distinct mountainous areas was studied using random amplified polymorphic (RAPD). Based on its morphological characteristics, the powdery mildew fungus presented high similarities to E. pisi. Molecular characterization of the ITS rDNA of E. pisi produced 650 bp nucleotides, PMITS (powdery mildew-internal transcribed region) primers produced 700 bp nucleotides, and an Erysiphe specific ITS primer pair amplified and synthesized 560 bp nucleotides. According to the findings, the collected E. pisi strains exhibited a low level of genetic diversity and only a slight differential in virulence on the host. In the study, E. pisi isolates from Anumapuram, Emerald Valley, Indira Nagar, and Thuneri showed a greater disease incidence in the natural field conditions and shared the same genetic lineage with other isolates in UPGMA hierarchical cluster analysis based on RAPD markers. There was no evidence of a link between the occurrence of the disease and these grouped populations.

Are the vegetables grown in the soil of municipal solid waste dumping sites safe for human health? An assessment from trace elements contamination and associated health risks

Trace elements containing untreated municipal solid waste discharged from improper management impose serious environmental and human health risks. In this work, the extent of trace elements (As, Pb, Cd, Fe, Mn, Zn) pollution in the soils and some common vegetables/food crops grown in the municipal solid waste dumping sites in southwestern Bangladesh (Khulna) were evaluated for the first time. To assess the human consumption’s suitability of the collected vegetables cultivated in the waste dumping sites, health risks were appraised due to the trace elements contamination. The concentration of trace elements in the collected soil and vegetable/food crop samples was analyzed by Atomic Absorption Spectrophotometer. The soil pollution evaluation indices such as geoaccumulation index (Igeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI) were used to evaluate the soil pollution level. The results showed that trace element levels in the soils and vegetables/food crops follow the order of Fe > Zn > Pb > Mn > As > Cd. The outcome of the soil pollution indices (Igeo, EF, CF, and PLI) implies potential trace elements pollution in the dumping sites, dominated by Pb and Zn. The multivariate analyses exhibited that the trace element sources are of natural and anthropogenic origin. The estimated daily intake of the trace elements via vegetables was lower than the maximum tolerable daily intake, except for Pb. The target hazard quotients of Pb exceeded the threshold level through the consumption of all cultivated vegetables, indicating significant health risks to the residents of the study area. Hence, effective measures should immediately be taken to control the trace elements pollution due to municipal waste dumping in southwestern Bangladesh to protect the environment and human health.

Growth Rate and Biomass Accumulation in Forage Maize (Zea mays), Forage Millet (Echinochloa utilis), Elephant Grass (Pennisetum purpureum) and Gamba Grass (Andropogon gayanus)

Background: Understanding the biomass accumulation and growth rate of the forages to feed the animal throughout the year is critical for improved earnings and long-term livestock production. This was found in forage maize (Zea mays), forage millet (Echinochloa utilis), elephant grass (Pennisetum purpureum) and Gamba grass (Andropogon gayanus). The experiment was place at Federal University Oye Ekiti in Ekiti State, Nigeria, in latitude N 07° 48.308, longitude E 005° 29.573 and elevation 548.4 meters above sea level, with an annual rainfall of 1778 mm. Methods: Planting four rows in a complete randomized design (CRD with four replications) was used to test the forage crops. Before planting, the soil in the experimental field had 49.96% organic matter and 33.59% after the crops were harvested. During the growth season, Pennisetum purpereum had the maximum plant height and sward height. Result: The Zea mays plant had the maximum leaf breadth (5.44 cm at 8 weeks) and the most leaves. Zea mays had the largest biomass accumulation (BC) of 113.80% after 8 weeks, followed by Andropogon gayanus with 67.15 per cent, Pennisetum purpureum with 56.24 per cent and Echinochloa utilis with 51.63%. Although there were no significant differences p£0.05 in the crude protein levels of the different species, samples from the latest cuts (8th week) had the greatest crude protein content (11.88% in Andropogon gayanus). Crude protein levels were shown to rise in lockstep with grass growth. Although there was no significant difference between the grass species, the crude fibre content of the three grass species was discovered to vary with the growth phase. The greatest fibre content of 13.58% in Zea mays was reported during the 6th week, which might be attributable to lignin encrustation in grasses that were harvested late. All of the plant samples of crops showed the same irregular pattern in crude fiber, moisture content and fat content, with no significant variance.

Nutrition of free-living Neotropical psittacine nestlings and implications for hand-feeding formulas.

Psittacidae is one of the most endangered families of birds in the world. Knowledge of their nutrition is important for understanding their survival and productivity in the wild, as well as for their adequate husbandry under human care. Hand-rearing is a common practice for psittacines, however research on their nutrition is limited. We analysed the predicted metabolisable energy, crude protein, crude fat, mineralsand the essential amino acid profiles of the crop contents from free-living nestlings of scarlet macaws (Ara macao) and red-and-green macaws (Ara chloropterus) from southeastern Peru, Cuban Amazons (Amazona leucocephala bahamensis) from the Bahamas, lilac-crowned Amazons (Amazona finschi) from northwestern Mexicoand thick-billed parrots (Rhynchopsitta pachyrhyncha) from northern Mexico. The crop content of the different parrot species displayed remarkably similar nutritional profiles, considering their diversity in habitats, geographic rangesand food sources. The crude protein and crude fat concentrations in crop samples were particularly similar for the Ara and Amazona species, while the thick-billed parrot stood out for its higher crude fat and lower crude protein content. Wider variations were found among the concentrations of sodium (Na) and iron (Fe), prolineand tryptophan. Compared with the requirements of 6-12weeksleghorn chickens, all free-ranging parrot diets contained lower crude protein, calcium (Ca), potassium (P)and Na concentrations. The hand-feeding formulas contained lower crude fat, magnesium (Mg), arginine, valine and phenylalanine concentrations, as well as much higher levels of Ca and zinc (Zn), in comparison with parrot crop samples. Our data suggest that a single formulation could be used to hand-rear Ara and Amazona sp. of 3weeks of age and older, while a different formulation would likely be more appropriated for Rhynchopsitta sp. Experimental studies should evaluate if increasing the concentration of crude fat, Mg, arginine, valineand phenylalanine enhances psittacine chick growth and health.

P-AgBot: In-Row &amp; Under-Canopy Agricultural Robot for Monitoring and Physical Sampling

In this work, we present a novel agricultural robot called the Purdue AgBot or P-AgBot that has been designed for in-row and under canopy crop monitoring and physical sampling. We suggest approaches to autonomous navigation, crop monitoring, and crop sampling that can be applied in crop rows and under canopies for different agricultural environments. Each monitoring approach was designed to extract key morphological characteristics of the crops. The proposed approaches of P-AgBot have been experimentally verified not only in simulation but also with real corn and sorghum crops. Crop heights and stalk diameters are able to be estimated in real-time with less than 10% error. Vision-based detection of leaf samples was implemented and physical sampling is accomplished with a more than 80% success rate.

A sample selection method based on similarity measure and fuzziness for crop classification from hyperspectral data

Crop mapping using remote sensing data is essential for predicting crop yield and future requirements. However, it can be accomplished by having full ground truth data corresponding to the samples captured by remote sensors. In a practical scenario, generally, we have limited ground-truth data where available crop samples are less than the number of spectral bands of a hyperspectral image. This paper proposes a sample selection method to select samples from a hyperspectral image based on similarity measure and fuzziness in the samples. We use a single-ground truth pixel of each crop for crop-sample selection. Firstly, we select the number of samples for each crop class using Spectral Information Diversion and tangent of Spectral Angle Mapper (SStan) measure from a selected region of the hyperspectral image. Then, we compute fuzziness for each selected crop sample to generate a set of final samples. We have evaluated the proposed method on an Airborne Visible near InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset and two benchmark datasets, i.e., Indian Pines and Salinas. Finally, classification results using three supervised classifiers, namely, support vector machine, k-nearest neighbourhood and random forest, have been shown in terms of overall accuracy and kappa coefficient. It can be demonstrated that the proposed method outperforms some prevailing methods by at least 4.5% for 5 samples and 11% for 10 samples for the Indian Pines dataset.

Assessment of lead toxicity in diverse irrigation regimes and potential health implications of agriculturally grown crops in Pakistan

In many countries, where wastewater irrigation has become a common practice, the accumulation of heavy metals by crops has been regarded as a severe environmental hazard. The evaluation of the toxic element lead (Pb) in water, agricultural soils and food crops, and their potential damage to human health, is the focus of the present study. Samples of eleven food crops, soil, with three treatments, waste, canal, and tube well waters, were collected from three feeding sites, Sargodha city, Sahiwal, Shahpur, and evaluated for Pb concentration. The results indicates that the range of Pb in the water, soil and crop samples were from 0.023 to 0.039 mg/L, 2.932–13.687 mg/kg and 1.241–4.825 mg/kg, respectively. The metal concentration was significantly higher (P < 0.001) in wastewater treated soil and crop samples that exceed the permissible limit of the World Health Organization (WHO). There is a positive correlation between the Pb concentration in the soil and crop samples. The pollution load index and enrichment factor are greater than 1, indicating a high level of soil contamination and the influence of natural and anthropogenic sources of Pb metal accumulation in soils. The bioconcentration factor (BCF), daily intake of metal (DIM), health risk index (HRI) and metal values are less than 1, indicating that toxic Pb are present in lower quantities in food crops and had no health risks to consumers. In wastewater treated soils, there is a significant accumulation of Pb toxicity. Therefore, it is vitally important to address Pb contamination and its potential entrance route into the human food chain.

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Mining activities have increased the accumulation of heavy metals in farmland soil and in food crops. To identify the key soil properties influencing heavy metal bioavailability and accumulation in food crops, 81 crop samples and 81 corresponding agricultural soil samples were collected from rape, wheat, and paddy fields. Heavy metal (copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), iron (Fe), and manganese (Mn)) concentrations in soils and rape, wheat, rice grains were determined using inductively coupled plasma atomic emission spectroscopy, and soil physicochemical properties (pH, organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium (AK)) were analyzed. Soil extractable metals were extracted using various single extractants (DTPA, EDTA, NH4OAc, NH4NO3, and HCl). The average concentrations of Cu, Zn, Pb, Cd, and Mn in the soil samples all exceeded the local geochemical background value (background values of Cu, Zn, Pb, Cd, and Mn are 43.0, 81.0, 28.5, 0.196, and 616 mg/kg, respectively), and Cd over-standard rate was the highest, at 98%. Furthermore, soil total Cd concentrations (0.1–24.8 mg/kg) of more than 86% of the samples exceeded the soil pollution risk screening value (GB 15618-2018). The sources of Cu, Zn, Pb, Cd, and Mn in soils were mainly associated with mining activities. The key factors influencing heavy metal bioavailability were associated with the types of extractants (complexing agents or neutral salt extractants) and the metals. Cd and Pb concentrations in most wheat and rice grain samples exceeded the maximum allowable Cd and Pb levels in food, respectively, and Cd concentrations in approximately 10% of the rice grain samples exceeded 1.0 mg/kg. Furthermore, rice and wheat grains exhibited higher Cd accumulation capacity than rape grains, and despite the high soil Cd concentrations in the rape fields, the rape grains were safe for consumption. High soil pH and AK restricted Cd and Cu accumulation in wheat grains, respectively. Soil properties seemed to influence heavy metal accumulation in rice grains the most.

NLow matrix effect pretreatment method based on gas-liquid micro-extraction technique for determining multi-class pesticides in crops

A low matrix effect pretreatment method by Gas-liquid microextraction technique (GLME) combined with ultrasound assisted extraction (UAE-GLME) has been developed for fast analysis of 41 multi-class pesticides in different crop samples. Extraction and cleanup procedures were completed within 20 min, and the impact of matrix effects in potato, lentil, corn and soybean matrices were systematically evaluated. As high as 97.6% pesticides treated by UAE-GLME experienced low matrix effect, and up to 99% pesticides except parathion-methyl in lentil samples were categorized as medium matrix effect. Good trueness (recoveries of 61.3–115.8%) and precision (RSD≤20%) were demonstrated by the UAE-GLME method, with MLOQs ranged from 0.005 to 0.02 mg kg−1. This method detected 2-phenylphenol, pyrimethanil, triadimefon and tebuconazole in the range of 0.008–0.012 mg kg−1 in real food samples. The multiresidue analysis feature of GLME has been validated, which displays further potential for high-throughput determination of organic pollutants in foods with distinctive properties.

Determination of Critical Concentrations of Boron in Soils and Plants of Cauliflower (Brassica oleracea var. botrytis L.) Using a Polynomial Equation

ABSTRACT An experiment was conducted to understand plant available B status in Entisols and Inceptisols of the Gangetic alluvial plains for cauliflower and to determine critical concentrations of B in soils and plants of cauliflower by using polynomial equations. Soil and cauliflower crops (plant and curd) were collected from 81 cauliflower growing farmers’ fields. Soil samples were analyzed for available B by using hot-CaCl2 (HCC), KH2PO4 (PDP), tartaric acid (TA) and HCl (HCl) and cauliflower crop samples for B concentration. Hot-CaCl2 and dilute HCl extracted a higher amount of B followed by TA and PDP in decreasing order. Better relationships of HCC and HCl with soil properties, cauliflower curd and plant weight and B concentration in crop tissues proved their superiority over TA and PDP for assessing B availability for cauliflower in soil. The critical level of adequate HCC and HCl extractable B concentrations were 0.53 and 0.54 mg kg−1 in Entisols and 0.57 and 0.58 mg kg−1 in Inceptisols, respectively. Similarly, critical B concentration in cauliflower plant was 21.5 and 23.8 mg kg−1 in Entisols and Inceptisols, respectively. The novel approach of determination of critical limits of B by using polynomial equations was found effective.

Quantifying the relationships of soil properties and crop growth with yield in a NPK fertilizer application maize field

Spatial variability of soil properties, crop performance, fertilization management, and tillage methods play an important role in yield prediction and improvement. However, the farm nutrient management in most regions of the world is still based on conventional and uniform management strategies, without take into consideration the spatial variability of the above factors. Therefore, the objectives of this study were to delineate potential management zones (MZs), with the considering of spatial variability of soil properties, crop performance, fertilization management, tillage methods, and investigate their relationships with yield, and their contributions to yield in each management zone. A 1.60 ha maize field with fertilizer and tillage management, in Lishu County, Jilin Province, China, was selected as the study area and soil samples with soil organic matter, total nitrogen and soil pH measured values, crop samples with crop height, leaf nitrogen content, above ground biomass at V6, silking, and maturity growth stages, and yield measured values were collected in 2019 and 2020. Firstly, the rectangular MZs were defined to divide the study area into relatively homogeneous areas based on the spatial variability of soil properties, crop performance, and yield of 2019 and 2020. Secondly, the correlations of soil properties and crop performance with yield were obtained across MZs and growth stages in 2019 and 2020. Lastly, the yield prediction models were established based on the above four types of factors by using random forest (RF) model and the contributions of main factors were obtained quantitatively across different MZs and years. The results showed that (1) the small maize field was divided into three rectangular MZs and most of the soil and crop attributes presented homogeneous characteristics in each zone; (2) correlations of soil properties and crop performance with yield varied across different MZs and growth stages, and even different years; (3) RF model obtained good performance across MZs and years, with R2 = 0.86 and RMSE = 0.77 kg/ha in 2019 and R2 = 0.89 and RMSE = 1.04 kg/ha in 2020 both for the whole field. The contribution of main factors for yield varied across both MZs and years, while some characteristics of the main factors’ contributions remain unchanged. This study would open the opportunity to farmers and agricultural experts for site-specific nutrient management in the maize field.