Kernel Oil Content Research Articles

Genetic analysis of maize crude fat content by multi-locus genome-wide association study

Crude fat is an important nutritional component of maize kernels. However, the genetic mechanisms underlying crude fat content in maize kernels remain elusive. Previous studies used single-model genome-wide association studies (GWAS) with limited population sizes, which can result in false positives of loci and hinder the identification of functional genes. Therefore, this study used a population consisting of 495 maize inbred lines, combined with 1.25 million single nucleotide polymorphisms (SNPs), and implemented GWAS using six models to identify quantitative trait nucleotides (QTNs) controlling crude fat content and to mine key genes. The results revealed a wide variation in crude fat content (0.62–16.03%) and broad-sense heritability (96.23%). In total, 744 significant QTNs were detected, with 147 co-located across different models, environments, and methods. Based on the 147 co-located QTNs, candidate genes were searched at 50 kb up- and downstream intervals of each QTN. We finally screened eight candidate genes (GRMZM2G169089, GRMZM2G117935, GRMZM2G002075, GRMZM2G368838, GRMZM2G058496, GRMZM2G090669, GRMZM2G001241, and GRMZM2G333454) related to crude fat content that exhibited high expression levels during kernel development in maize inbred line B73. Notably, GRMZM2G169089, GRMZM2G117935, GRMZM2G002075, and GRMZM2G368838 are involved in the linoleic acid metabolic pathway, oil metabolism, kernel growth, and development in maize. Furthermore, co-expression network analysis revealed that the eight candidate genes exhibited strong correlations with 30 known genes. Proteins encoded by candidate genes interact with various other proteins and play an important role in oil content and oleic acid metabolism in maize kernels. The best haplotypes of candidate genes might increase crude fat content without decreasing maize yield. These results broaden the understanding of the genetic mechanism of crude fat content and facilitate marker-assisted selection for high-crude fat breeding programs for maize.

Rapid detection of oil content in Camellia oleifera kernels based on hyperspectral imaging and machine learning

The oil content (OC) of kernels is one of the primary targets in the breeding of Camellia oleifera. However, the OC determination is labor-consuming and time-costing using traditional methods. In this study, a rapid and efficient OC detecting method was developed based on hyperspectral imaging (HSI). The OCs of 220 C. oleifera clones were first determined using the Soxtec extraction method and hyperspectral images of all samples were obtained. Five spectral preprocessing methods and two dimensionality reduction methods was performed to eliminate hyperspectral noise. Based on the preprocessed spectral and OC data, OC predictive models were developed. The optimal OC prediction model was developed based on the characteristic wavelengths selected by competitive adaptive reweighted sampling from the preprocessed data by Savitzky–Golay smoothing and the first derivative method. The determination coefficient of this model was 0.9383, with a root mean squared error prediction of 1.7921 % and residual predictive deviation of 4.0271. The further validation of this model by the other samples demonstrated it’s robustness and accuracy. The results reveal the potential of HSI in the rapid OC detection in C. oleifera. This will provide reference and guidance for the phenotype collection of C. oleifera.

Impact of Mid-season Moisture Stress on Yield Attributes and Seed Quality in Groundnut Genotypes

A field experiment was conducted at Regional Agricultural Research Station, Tirupati, Acharya N.G. Ranga Agricultural University during rabi, 2022 and 2023 to study the impact of mid-season moisture stress (40-80 DAS) on yield attributes and quality traits of groundnut genotypes. The experiment was carried out in randomized block design with factorial concept with two main treatments i.e., control and mid-season moisture stress (40-80 DAS) and 12 sub treatments i.e., two check varieties and ten contrasting groundnut genotypes in three replications. Among the groundnut genotypes and check varieties tested for their tolerance to moisture stress the genotypes, TCGS 2326, TCGS 1792, K9 and TCGS 2333 were found to withstand mid-season moisture stress and maintained highest 100 kernel weight, shelling percentage, protein and oil content indicating tolerance to moisture stress.

Effects of Different Weeding Methods on Soil Physicochemical Properties, Root Morphology, and Fruit Economic Traits in Camellia oleifera Abel. Plantations

Soil physicochemical properties, root characteristics, and fruit economic traits were determined in Camellia oleifera plantations under spontaneous vegetation + mowing (W1), spontaneous vegetation + glyphosate (W2), and no weeding (CK) treatments. Compared with CK, W1 reduced soil bulk density and increased total nitrogen, total phosphorus, ammonium nitrogen, nitrate nitrogen, and effective potassium content. W2 treatment resulted in higher bulk density than W1 and lower water-holding capacity, total nitrogen, total phosphorus, total potassium, ammonium nitrogen, nitrate nitrogen, and available potassium of the soil. Generally, both W1 and W2 inhibited weed morphological traits while favoring the C. oleifera root system, with the W1 treatment resulting in the greatest increase. Fruit transverse diameter, longitudinal diameter, yield, and oil yield were higher in W1 than in CK and W2 treatments. Weed root systems and C. oleifera root systems ultimately affect oil production and yield by affecting bulk density, ammonium nitrogen, nitrate nitrogen, fruit transverse diameter, seed yield, and seed kernel oil content. In summary, W1 treatment improved the physicochemical properties, root growth, fruit growth, and soil quality in C. oleifera plantations.

Mining of Oil Content Genes in Recombinant Maize Inbred Lines with Introgression from Temperate and Tropical Germplasm.

The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The MPP used the subtropical maize inbred lines CML312 and CML384, along with the tropical maize inbred lines CML395, YML46, and YML32 as the female parents, and Ye107 as the male parent. A genome-wide association study (GWAS) was performed using 429 RILs of the multiparent population across three environments, employing 584,847 high-quality single nucleotide polymorphisms (SNPs). Furthermore, linkage analysis was performed in the five subpopulations to identify quantitative trait loci (QTL) linked to TOC in maize. Through QTL mapping and GWAS, 18 QTLs and 60 SNPs that were significantly associated with TOC were identified. Two novel candidate genes, Zm00001d029550 and Zm00001d029551, related to TOC in maize and located on chromosome 1 were reported, which have not been previously reported. These genes are involved in biosynthesis, lipid signal transduction, plant development and metabolism, and stress responses, potentially influencing maize TOC. Haplotype analysis of Zm00001d029550 and Zm00001d029551 revealed that Hap3 could be considered a superior haplotype for increasing TOC in maize. A co-located SNP (SNP-75791466) on chromosome 1, located 5648 bp and 11,951 bp downstream of the candidate genes Zm00001d029550 and Zm00001d029551, respectively, was found to be expressed in various maize tissues. The highest expression was observed in embryos after pollination, indicating that embryos are the main tissue for oil accumulation in maize. This study provides a theoretical basis for understanding the genetic mechanisms underlying maize TOC and developing high-quality, high-oil maize varieties.

Advancements in breeding for high oil content in maize (Zea mays. L)

Maize is one of the world’s most important food crops, supplying>5% deity energy. Due to its wider adaptability, high yield potential, maize is widely utilized as food and animals world worldwide. It enriched with caloric starch and the high energy in its oil. The kernel oil content in commercial high-oil maize hybrids averages ∼8%, far lower than that in developed high-oil maize lines (as high as 20%). The maize seed/kernel consisted of endosperm (82%) and the germ (embryo and scutellum) (12%). The germ region makes up 80–84% of kernel oil, with the aleurone and endosperm making up 12% and 5%, respectively. High-oil maize may serve as a valuable forage maize germplasm for breeding. Maize oil is also considered as high-quality oil for human health due to the high proportion of polyun-saturated fatty acids. Selection for high oil increases the proportion of germ and further content of germ oil. The problems that over-shadow the successful production of high-oil corn are low grain yield potential, physiological cost of oil synthesis, low seed vigor, low kernel weight, shorter seed longevity, and poor germination of high-oil corn lines. It is necessary to make efforts to generate nested populations or use marker-assisted recurrent selection (MARS) to accumulate the advantageous quantitative trait loci (QTLs) in the pa-rental lines. The oil content of maize inbreeds and hybrids could be further improved with the use of transgenes, genomics, and metabolic engineering technologies in new insight for the development of high oil maize breeding and strategies.

Assessment of Groundnut (Arachis hypogaea L.) Cultivars on Yield and Yield Contributing Traits using Principal Component Analysis

Background: The breeder’s ability to identify and exploit genetic diversity at the genotypic level is essential for the success of any breeding effort. It requires evaluating various treatments for a variety of traits in order to choose parents with the greatest genetic diversity. The purpose of this research is to characterize the genetic variation among 48 groundnut accessions with 6 checks. Methods: Experiment was conducted to study groundnut genotypes for 15 varied traits such as phenological, yield attributing and biochemical ones. Data analysis for principal component analysis (PCA) is done using R software. Result: Principal component analysis (PCA) was carried out to investigate the principal components of variance usingtraits of study. Total variance among characters are classified into 15 principal components. Out of which first five components have eigen value greater than 1 contributing to 65.9% total variance. PC 1 alone accounted for highest variance of 16.3%, followed by PC 2 with 12.6%. PC1 explained a large amount of variation in important traits as number of mature kernels per plant, total number of kernels per plant, pod yield per plant and kernel yield per plant. PC 2 have positive influence on days to 50% flowering, days to maturity, number of branches per plant, biological yield per plant, number of mature kernels per plant, total number of kernels per plant, sound mature kernel percentage, oil content and harvest index.

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data. Initially, the study automatically screening the best model from up to 504 algorithm combinations. Subsequently, multi-stage grid search is utilized for improving precision. We collected 270 kernel samples from different parts of the ear from 15 high oil and regular corn materials, with oil contents ranging from 1.4% to 13.1%. Experimental results show that the combinations SG + NONE+KS + PLSR(R2: 0.8570) and MA + LAR+Random+MLR(R2: 0.8523) performed optimally. After parameter optimization, their R2 values increased to 0.9045 and 0.8730, respectively. Additionally, the ACNNR model achieved an R2 of 0.8878 and an RMSE of 0.2243. The improved algorithm significantly outperforms traditional methods and ACNNR model in prediction accuracy and adaptability, offering an effective method for field applications.

Analysis and evaluation of Camellia oleifera Abel. Germplasm fruit traits from the high-altitude areas of East Guizhou Province, China

Camellia oleifera, a significant woody edible oil species, was examined using 48 germplasm resources from high-altitude regions in East Guizhou Province, China, to analyze fruit quality. The analysis aimed to identify high-performance germplasm, providing theoretical and research foundations for selecting and cross-breeding superior C. oleifera varieties in these regions. Fifteen primary traits of mature fruits were measured and analyzed, including four phenotypic traits (single fruit weight, transverse diameter, longitudinal diameter, peel thickness) and eleven quality traits (fresh seed yield rate, dry seed yield rate, dry kernel yield rate, seed kernel oil content, palmitic acid, palmitoleic acid, stearic acid, oleic acid, linoleic acid, α-linolenic acid, cis-11-eicosenoic acid). A comprehensive evaluation employing cluster and principal component analyses (PCA) was conducted. The cluster analysis categorized the germplasms into five groups at a squared Euclidean distance of 14, with the first category comprising 17 germplasms, the second 28, and the third, fourth, and fifth each containing one. PCA reduced the 15 traits to five principal components (PCs), with PC1 having the highest eigenvalue of 3.57 and a contribution rate of 23.8%, mainly representing phenotypic traits. PC2, contributing 20.44%, represented linoleic acid, while PC3, PC4, and PC5, with contribution rates of 12.99%, 9.13%, and 7.45% respectively, predominantly represented seed kernel oil content, fresh seed yield, and palmitoleic acid. Employing a weighted sum method, a comprehensive evaluation function was developed to calculate total scores for each superior individual, forming the basis for rankings and selections. Notable variability was detected in single fruit weight, peel thickness, and fresh and dry seed yields, while oleic acid exhibited the lowest coefficient of variation. Dry seed yield showed a robust positive correlation with seed kernel oil content and the concentrations of palmitic and linoleic acids, whereas seed kernel oil content was inversely correlated with cis-11-eicosenoic acid levels. Five PCs with eigenvalues > 1 were identified, highlighting the top ten superior individuals: QD (Qian Dong: the code of eastern Guizhou Province)-33 > QD-34 > QD-48 > QD-38 > QD-27 > QD-15 > QD-35 > QD-5 > QD-14 > QD-36. Thus, the 48 C. oleifera germplasms from East Guizhou’s high-altitude areas demonstrate significant potential for enhancing traits such as single fruit weight, peel thickness, and fresh and dry seed yields. Specifically, QD-33, QD-34, and QD-48 exhibited superior comprehensive performance, designating them as prime candidates for variety selection and breeding.

The Biosynthesis Pattern and Transcriptome Analysis of Sapindus saponaria Oil.

The Sapindus saponaria (soapberry) kernel is rich in oil that has antibacterial, anti-inflammatory, and antioxidant properties, promotes cell proliferation, cell migration, and stimulates skin wound-healing effects. S. saponaria oil has excellent lubricating properties and is a high-quality raw material for biodiesel and premium lubricants, showing great potential in industrial and medical applications. Metabolite and transcriptome analysis revealed patterns of oil accumulation and composition and differentially expressed genes (DEGs) during seed development. Morphological observations of soapberry fruits at different developmental stages were conducted, and the oil content and fatty acid composition of the kernels were determined. Transcriptome sequencing was performed on kernels at 70, 100, and 130 days after flowering (DAF). The oil content of soapberry kernels was lowest at 60 DAF (5%) and peaked at 130 DAF (31%). Following soapberry fruit-ripening, the primary fatty acids in the kernels were C18:1 (oleic acid) and C18:3 (linolenic acid), accounting for an average proportion of 62% and 18%, respectively. The average contents of unsaturated fatty acids and saturated fatty acids in the kernel were 86% and 14%, respectively. Through the dynamic changes in fatty acid composition and DEGs analysis of soapberry kernels, FATA, KCR1, ECR, FAD2 and FAD3 were identified as candidate genes contributing to a high proportion of C18:1 and C18:3, while DGAT3 emerged as a key candidate gene for TAG biosynthesis. The combined analysis of transcriptome and metabolism unveiled the molecular mechanism of oil accumulation, leading to the creation of a metabolic pathway pattern diagram for oil biosynthesis in S. saponaria kernels. The study of soapberry fruit development, kernel oil accumulation, and the molecular mechanism of oil biosynthesis holds great significance in increasing oil yield and improving oil quality.

Evaluation of Food Quality of Released Maize Varieties Grown in Oromia, Ethiopia

Maize is widely produced in Ethiopia and is mostly used for human consumption. Considerable efforts are being made by agricultural researchers to release different maize varieties and adaptations according to different agro-ecology. However, there is an information gap on the physic-chemicals of food qualities and the consumer's preference for all maize varieties. Thus, this study was conducted to evaluate the food quality of released food maize varieties through physical, chemical and sensory evaluation. Fifteen released maize varieties were collected from different agricultural research centers. The physicochemical attributes of these varieties were analyzed with three replications. Sensory evaluation was also performed by using the hedonic scale method. Thousand kernel weight, moisture, oil, protein, starch, ash, sodium, calcium potassium, and phosphorus contents were determined in the range of 152.81 – 479.45 grams, 9.54 – 12.97%, 3.78 – 4.86%, 7.07 – 11.76%, 77.75 – 81.27%, 0.64 – 1.12%, 228.75 – 3.02.33 ppm, 225.65 – 332.34 ppm, 1626.34 – 2714.51 ppm and 956.95 – 1452.86 ppm, 305.42 – 716.91 ppm and 811.50 – 1731.10 ppm, respectively. Overall acceptability of porridge and Injera prepared from maize varieties were on the scale of neither like nor dislike to like moderately. There were significant (P<0.05) variations in physical, chemical and organoleptic properties due to maize varieties variation. BH540, BH661 and Limu maize varieties had higher starch content and white color that gave them better acceptance value than those varieties with higher protein content and yellow color such as Melkasa 1, Melkasa seven, and Melkasa IQ maize varieties.

Effect of Growth Retardants on Plant Yield and Seed Quality of Groundnut (Arachis hypogaea L.)

A field experiment was carried out during Kharif, 2023 at dryland farm of S. V. Agricultural College, Tirupati campus of Acharya N. G. Ranga Agricultural University, Andhra Pradesh. The experiment was laid out in a split-plot design with three replications and two Genotypes as main factor (G1- Dharani, G2- K-6), growth retardants as sub factor (T1- Control + RDF of NPK, T2- MH @ 2000 ppm at 20 DAS, T3- MH @ 2000 ppm at 45 DAS, T4- PBZ @ 250 ppm at 20 DAS, T5- PBZ @ 250 ppm at 45 DAS , T6- ABA @ 250 ppm at 20 DAS, T7- ABA @ 250 ppm at 45 DAS, T8- ABA @ 500 ppm at 20 DAS, T9- ABA @ 500 ppm at 45 DAS, T10- CCC @ 5000 ppm at 20 DAS, T11- CCC @ 5000 ppm at 45 DAS). The current experiment was conducted with an objective to study the impact of growth retardants on reduction of plant stature thereby enhancing the reproductive and assimilates partitioning efficiency. Results revealed that application of T5- PBZ @ 250 ppm at 45 DAS recorded higher Pod yield, Kernel yield, 100 kernel weight, protein content and oil content.

Effect of plant density on total oil quality and fatty acid compositions in peanut (Arachis hypogaea L.) cultivars

Research in the Eastern Mediterranean Transition Region of Türkiye has demonstrated that plant density impact yield of peanut (Arachis hypogaea L.) cultivars differentially. It is suspected that interactions of plant density and cultivar could also impact oil quality and fatty acid composition when grown in this region. This topic has not been addressed in the country; therefore, the objective of this research was to determine if plant density can affect those variables. The study was conducted in 2020 and 2021 in the zone of Osmaniye to determine total oil content of kernels, and contents of oleic acid, palmitic acid, arachidonic acid, linoleic acid, stearic acid, and lignoceric acid, as well as iodine value and oleic/linoleic ratio for the cultivars Halisbey, Rigel, Aysehanım, NC 7, and Masal. Plant response was in most instances different regardless of plant populations made up of a single row planting pattern with rows spaced 70 cm apart (95.000 plants ha-1) compared with a twin row planting pattern with rows spaced 20 cm apart on 90 cm centers with an intra-row distance of 15 cm (148.000 plants ha-1). It is concluded that no differences in oil quality and fatty acid composition were noted when comparing cultivars established at various combinations of planting pattern and plant population, but there were notable differences among the cultivars.

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks.

Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types. This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption. When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.

Enhancing Soil Fertility and Elevating Pecan Fruit Quality through Combined Chemical and Organic Fertilization Practices

This study focused on 6-year-old ‘Pawnee’ pecan trees to elucidate the differential responses of physicochemical properties of orchard soil and pecan fruit quality when combining chemical and organic fertilizers. The aim was to unveil the mechanisms that underlie the effects of different fertilization treatments on soil fertility, soil enzyme activities, and pecan fruit quality. Four treatments were established: sole chemical fertilizer (CF; N:P2O5:K2O is 15:15:15), chemical fertilizer combined with cake fertilizer (CF+CC), chemical fertilizer combined with manure fertilizer (CF+M), and chemical fertilizer combined with cake and manure fertilizer (CF+CC+M). Measurements were taken to assess the soil nutrient content, soil enzyme activities, and fruit growth quality in some orchards under different fertilization treatments. The results revealed that the combined application could increase yield and enhance pecan quality. Among these, the CF+M+CC treatment demonstrated the most favorable outcomes, with the pecan kernel oil and unsaturated fatty acid contents reaching 72.33% and 97.54%, respectively. The combined fertilization treatments had no significant impacts on soil trace elements such as Mg, Cu, and Mn; however, it significantly increased the Available Phosphorus (AP), Total Nitrogen (TN), Soil Organic Matter (SOM) and S-ACP (soil acid phosphatase) activities. In summary, the combined application of chemical and organic fertilizers can significantly increase the soil nutrient content and enzyme activities in pecan orchards, to promote the enhancement of fruit quality and economic aspects.

Hub Genes in Stable QTLs Orchestrate the Accumulation of Cottonseed Oil in Upland Cotton via Catalyzing Key Steps of Lipid-Related Pathways

Upland cotton is the fifth-largest oil crop in the world, with an average supply of nearly 20% of vegetable oil production. Cottonseed oil is also an ideal alternative raw material to be efficiently converted into biodiesel. However, the improvement in kernel oil content (KOC) of cottonseed has not received sufficient attention from researchers for a long time, due to the fact that the main product of cotton planting is fiber. Previous studies have tagged QTLs and identified individual candidate genes that regulate KOC of cottonseed. The regulatory mechanism of oil metabolism and accumulation of cottonseed are still elusive. In the current study, two high-density genetic maps (HDGMs), which were constructed based on a recombinant inbred line (RIL) population consisting of 231 individuals, were used to identify KOC QTLs. A total of forty-three stable QTLs were detected via these two HDGM strategies. Bioinformatic analysis of all the genes harbored in the marker intervals of the stable QTLs revealed that a total of fifty-one genes were involved in the pathways related to lipid biosynthesis. Functional analysis via coexpression network and RNA-seq revealed that the hub genes in the co-expression network that also catalyze the key steps of fatty acid synthesis, lipid metabolism and oil body formation pathways (ACX4, LACS4, KCR1, and SQD1) could jointly orchestrate oil accumulation in cottonseed. This study will strengthen our understanding of oil metabolism and accumulation in cottonseed and contribute to KOC improvement in cottonseed in the future, enhancing the security and stability of worldwide food supply.

The Effects of Oil Content on the Structural and Textural Properties of Cottonseed Butter/Spread Products.

Plant-based butters from nuts and seeds have steadily increased in consumer popularity due to their unique flavors and healthy nutritional properties. Oil content is a critical parameter to measure the proper consistency and stability of plant butter and spread products. Previous work has shown that glandless cottonseed can be used to formulate cottonseed butter products to increase the values of cottonseed. As part of the efforts made in the valorization of cottonseed, this work evaluated the effects of oil content on the microstructural and textural properties of cottonseed butter/spread products. While the oil content in the raw cottonseed kernels was 35% of the kernel biomass, additional cottonseed oil was added to make cottonseed butter products with six oil content levels (i.e., 36, 43, 47, 50, 53, and 57%). The values of three textural parameters, firmness, spreadability, and adhesiveness, decreased rapidly in an exponential mode with the increasing oil content. The particle size population in these butter samples was characterized by similar trimodal distribution, with the majority in the middle mode region with particle sizes around 4.5-10 μm. Higher oil content decreased the butter particle size slightly but increased oil separation during storage. The oxidation stability with a rapid oxygen measurement was gradually reduced from 250 min with 36% oil to 65 min with 57% oil. The results of this work provide information for the further optimization of formulation parameters of cottonseed butter products.

Seed and oil quality characteristics in castor (Ricinus communis L.)

Eighteen castor (Ricinus communis L.) genotypes (hybrids/varieties/parental lines) were studied for seed, oil quality as well as fatty acid profile. The maximum 100 seed wt (37.7 g) was observed in 48-1 genotype and the oil content (50.7%) was recorded in SKP-84 pistillate line. Hundred seed weight showed a positive correlation with kernel % (r=0.472*) and seed oil content has positive correlation with kernel oil content (r=0.664**). Industrially important oil qualities like specific gravity, colour, viscosity were also measured. The free fatty acid as oleic acid varied between 1.93 to 7.03%. Among the fatty acids, the ricinoleic acid content was ranging from 84.3 to 87.8% and the maximum was recorded in GCH-4 hybrid.

Effect of Gypsum, Nitrogen and Phosphorus on Growth, Yield and Quality of Spring Groundnut

Background: Groundnut kernels contain 48-50% edible oil, 25-34% protein, 10-20% carbohydrates and are a rich source of vitamins (E, K and B complex). Groundnut is a legume-oilseed crop, its requirement of phosphorus, calcium and sulphur is quite high. Gypsum is commonly used as a source of calcium and sulphur for groundnut all over the world. Keeping all these points in view the present study was undertaken to find out the optimum mineral nutrition of spring groundnut Methods: A field experiment was conducted for 2 years at the Students’ Research Farm, Department of Agronomy, Punjab Agricultural University, Ludhiana during spring season of 2018 and 2019. The experiment was laid out in a split plot design replicated three times with four levels of gypsum (0, 125, 175 and 225 kg ha-1) in combination with two gypsum application stages (Full at sowing and 50% at sowing + 50% at flower initiation stage) in the main plot and three levels of nitrogen and phosphorus (15 kg N ha-1 + 20 kg P2O5 ha-1, 25 kg N ha-1 + 30 kg P2O5 ha-1 and 35 kg N ha-1 + 40 kg P2O5 ha-1) in the sub-plot. Result: Application of 225 kg ha-1 gypsum resulted significantly higher growth parameters, pod yield, haulm yield, kernel yield over other gypsum levels. Growth parameters, pod yield, haulm yield and kernel yield were significantly higher with the split application as compared to basal application of gypsum. Growth parameters viz., emergence count, plant height, number of branches plant-1 and dry matter accumulation were increased with increase in the levels of nitrogen and phosphorus up to 25 kg N ha-1 + 30 kg P2O5 ha-1. However, 35 kg N ha-1 + 40 kg P2O5 ha-1 resulted significantly higher protein and oil content of kernels over other levels of nitrogen and phosphorus, while pod yield, haulm yield and kernel yield were at par with 25 kg N ha-1 + 30 kg P2O5 ha-1 during both the years.

Effects of Soil Properties and Nutrients on the Fruit Economic Parameters and Oil Nutrient Contents of Camellia oleifera

The Camellia oleifera industry is hindered by the substandard quality of its fruits and the low yield of camellia seed oil. Although soil factors have been shown to affect the productivity of this plant, the relationship between C. oleifera characteristics and soil properties and nutrients remains unclear. Therefore, we investigated soil factors within the central distribution area of this species. Our findings revealed that this plant thrives in acidic soils with a medium cation exchange capacity. There were moderate differences in the main and medium element contents in the soils, while the variation of microelements was significant. Overall, C. oleifera cultivated soils were poor, with an uneven distribution of soil nutrients. Most of the shape characteristics of camellia fruits showed moderate variability, whereas dry kernel rate and oil content exhibited minor variability. The fatty acid profiles remained stable across different planting sites, but there were higher variations in the content of active compounds. Fruit shape characteristics were primarily influenced by soil properties, while soil nutrients mainly affected the seeds and kernels of the fruit. Minor fatty acid content could be influenced by soil properties and nutrients, except for total nitrogen (TN), which specifically affected the content of palmitic acid and oleic acid. There was no significant correlation between soil factors and sterols, polyphenols, and tocopherols, while squalene was affected by soil properties. Our study highlights the importance of considering soil properties and nutrients in the cultivation of C. oleifera and emphasizes the need for rational fertilizer application.