High Seed Protein Content Research Articles

The potential of the amaranth collection maintained at VIR in the context of global plant breeding and utilization trends

The potential of the amaranth collection maintained at VIR in the context of global plant breeding and utilization trends

The Germination Performance After Dormancy Breaking of Leucaena diversifolia (Schltdl.) Benth. Seeds in a Thermal Gradient and Its Distribution Under Climate Change Scenarios.

Climate change models predict temperature increases, which may affect germination, an important stage in the recruitment of individuals in agroecosystems. Therefore, it is crucial to conduct research on how temperature will impact the germination of multipurpose native species. Leucaena diversifolia (Schltdl.) Benth. is native to America and is commonly cultivated around the world due to having a high protein content in seeds, and their trees are used in agrosilvopastoral systems because they fix nitrogen and provide shade and cattle feed. However, climate change affects the critical phases of its life cycle and influences its growth, reproduction, phenology, and distribution. To assess the germination performance of Leucaena diversifolia under different temperatures throughout thermal times, we estimated germination variables and determined cardinal temperatures and thermal time; we also analysed germination and potential distribution under two climate change scenarios. We found significant variations in seed germination (78-98%) and differences in cardinal temperatures (Tb = 5.17 and 7.6 °C, To = 29.42 and 29.54 °C, and Tc = 39.45 and 39.76 °C). On the other hand, the sub-optimal and supra-optimal temperature values showed little differences: 51.34 and 55.57 °Cd. The models used showed variations in germination time for the analysed scenarios and the potential distribution. We confirm that the populations and distribution of L. diversifolia will be altered due to climate changes, but the species retains the ability to germinate under warmer conditions.

High-protein soybean lines with stable seed protein content under heat and drought stresses

A significant factor determining the value of soybean is protein content, in addition to its importance as an oilseed. Increasing the seed protein content in soybean will make it a more valuable food and feedstock and enhance its importance in the international market. Due to this reason, developing high-protein soybean varieties is gaining increased industrial interest. To ensure profitable production, the stability of the high-protein trait needs to be verified under environmental stresses. The objectives of this study were to test whether the high-protein soybean genotypes can maintain their high seed protein content under heat and drought stresses imposed during seed filling and to assess the changes in their oil composition under stress conditions. The changes in seed protein and oil contents and oil composition of the high-protein lines NLM09–77, N14-7017, and N16-9924 under heat and drought stresses were assessed and compared against those of a recently released high-yielding, high-protein variety Benning HP; elite cultivars NC-Dunphy, NC-Raleigh, and NC-Dilday; low-protein breeding lines N11–9228 and N09-2505; heat-tolerant line DS25-1; and heat-susceptible line DT97-4290. We found that the high-protein genotypes NLM09–77, N16–9924, N14-7017, and Benning HP maintained their high seed protein contents under heat and drought stresses (46–55 %). Among them, NLM09–77 and N14-7017 were unique with no apparent negative relationship between the seed protein and oil contents. In terms of oil composition, N14-7017, NLM09-77, and Benning HP were similar to the elite cultivars NC-Dunphy and NC-Dilday. Additionally, the high-protein genotypes N14-7017, NLM09-77, and Benning HP did not exhibit any yield drag (2993–3150 kg ha−1vs 3289–3368 kg ha−1 for the elite cultivars). Taken together, our study demonstrates the usefulness of the advanced breeding lines NLM09–77 and N14-7017 and cultivar Benning HP for developing new varieties with enhanced nutritional value under climate change-associated stresses.

Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq.

Peanut (Arachis hypogaea L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (qSPCB10.1) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the qSPCB10.1 locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying qSPCB10.1. Further locus interaction analysis revealed that qSPCB10.1 could not affect the seed oil accumulation unless qOCA08.1XH13 was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.

The vacuolar sulfate transporter PsSULTR4 is a key determinant of seed yield and protein composition in pea.

Pea is a grain legume crop with a high potential to accelerate the food transition due to its high seed protein content and relatively well-balanced amino acid composition. The critical role of external sulfur (S) supply in determining seed yield and seed quality in pea makes it essential to understand the impact of whole plant S management on the trade-off between these two traits. Here, we investigated the physiological relevance of vacuolar sulfate remobilization by targeting PsSULTR4, the only pea sulfate transporter showing substantial similarity to the vacuolar sulfate exporter AtSULTR4;1. Five mutations in PsSULTR4 were identified by TILLING (Targeting Induced Local Lesions IN Genomes), two of which, a loss of function (W78*) and a missense (E568K), significantly decreased seed yield under S deprivation. We demonstrate that PsSULTR4 triggers S distribution from source tissues, especially lower leaves, to reproductive organs to maintain seed yield under S deficiency. Under sufficient S supply, sultr4 seeds display lower levels of the S-rich storage protein PA1 at maturity. They also overaccumulate sulfate in the endosperm at the onset of seed filling. These findings uncover a role of PsSULTR4 in the remobilization of vacuolar sulfate during embryo development, allowing the efficient synthesis of S-rich proteins. Our study uncovers that PsSULTR4 functions (i) in source tissues to remobilize stored vacuolar sulfate for seed production under low S availability and (ii) in developing seeds well supplied with S to fine-tune sulfate remobilization from the endosperm as a critical control point for storage activities in the embryo.

Association study of crude seed protein and fat concentration in a USDA pea diversity panel.

Pea (Pisum sativum L.) is a key rotational crop and is increasingly important in the food processing sector for its protein. This study focused on identifying diverse high seed protein concentration (SPC) lines in pea plant genetic resources. Objectives included identifying high-protein pea lines, exploring genetic architecture across environments, pinpointing genes and metabolic pathways associated with high protein, and documenting information for single nucleotide polymorphism (SNP)-based marker-assisted selection. From 2019 to 2021, a 487-accession pea diversity panel, More protein, More pea, More profit, was evaluated in a randomized complete block design. DNA was extracted for genomic analysis via genotype-by-sequencing. Phenotypic analysis included protein and fat measurements in seeds and flower color. Genome-wide association study (GWAS) used multiple models, and the Pathways Association Study Tool was used for metabolic pathway analysis. Significant associations were found between SNPs and pea seed protein and fat concentration. Gene Psat7g216440 on chromosome 7, which targets proteins to cellular destinations, including seed storage proteins, was identified as associated with SPC. Genes Psat4g009200, Psat1g199800, Psat1g199960, and Psat1g033960, all involved in lipid metabolism, were associated with fat concentration. GWAS also identified genes annotated for storage proteins associated with fat concentration, indicating a complex relationship between fat and protein. Metabolic pathway analysis identified 20 pathways related to fat and seven to protein concentration, involving fatty acids, amino acid and protein metabolism, and the tricarboxylic acid cycle. These findings will assist in breeding of high-protein, diverse pea cultivars, and SNPs that can be converted to breeder-friendly molecular marker assays are identified for genes associated with high protein.

The Performance of Agronomic and Quality Traits of Quinoa under Different Altitudes in Northwest of China

Quinoa is a resilient crop known for its adaptability to diverse environmental conditions. This study examined the agronomic performance and ecological adaptability of quinoa across four distinct altitudes in the northwestern regions of China. Six quinoa genotypes were assessed for agronomic traits, phenology and yield performances, and nutritional quality characteristics in Yining city, Nilka County, Tekes County and Zhaosu County under varied environmental conditions. Our findings demonstrate significant variations in all measured traits, including agronomic and nutritional quality traits, across the various altitudes and genotypes. In Yining city, a warmer and lower altitude area, genotypes exhibit superior traits for fodder production, including increased branches, longer panicles, robust stems, and high seed protein content. Genotypes Jinli 1 and Beijing 2 achieve yields exceeding 3 tons per hectare (t/ha), highlighting the region’s productivity. The Nilka and Tekes counties boasted the shortest quinoa crop cycles, with Jinli 1 yielding 4.05 t/ha seeds in Tekes County, exhibiting high protein and fat content alongside low saponins, making it a prime location for yield production. Zhaosu County, with its cooler climate and fertile chernozem soil, elicited high 1000 seed weight and more robust protein response than Nilka and Tekes counties, illustrating the impact of soil fertility on nutritional composition. Correlation analyses further elucidated that the plants characterized by shorter flowering times, crop cycles, compact inflorescence, and taller heights exhibited superior seed yields. Our study contributes significant insights into the ecological adaptability and nutritional dynamics of quinoa, with implications for sustainable crop production and food security in diverse agroecosystems.

Ecophysiological mechanisms underlying the positive relationship between seed protein concentration and yield in soybean under field heat and drought stress

AbstractA positive relationship between protein concentration and yield has been documented in different combinations of genotype and environment, often under potential conditions. However, the ecophysiological bases underlying this positive relationship under heat stress (HS) and drought stress (DS) during seed filling are still lacking. Our objective was to evaluate the relationship between seed protein content and concentration with yield in field experiments exposed to HS, DS and HS × DS interaction during the seed filling. Two field experiments were conducted and assimilates accumulation, remobilization and redistribution patterns were analysed in high and low seed protein soybean genotypes. The crop was exposed to four treatments: control (ambient temperature and soil water content near field capacity), HS (episodes above 32°C, 6 h d−1) during 15 days, DS (soil water content ≤25% of field capacity) during the whole seed filling and HS × DS. Significant and positive relationships between seed protein content and concentration with yield were observed across treatments and genotypes. Under DS and HS × DS, assimilates available during the seed filling decreased, and assimilates remobilization and partition to seeds were limited, responses significantly associated with seed protein content and concentration, and yield reductions. Furthermore, we demonstrated here that the high leaf N content at the beginning of seed filling, the short early reproductive phase duration, the high source to sink ratio and the high dry matter stem remobilization capacity, as well as the low seed number and high seed weight are intrinsic characteristics of the high protein genotype that could be associated with its high seed protein content and concentration and yield under stressful conditions. This knowledge is key to develop soybean management strategies to improve seed protein level and yield under contrasting productive scenarios.

Identification of QTLs associated with seed protein concentration in two diverse recombinant inbred line populations of pea.

Improving the seed protein concentration (SPC) of pea (Pisum sativum L.) has turned into an important breeding objective because of the consumer demand for plant-based protein and demand from protein fractionation industries. To support the marker-assisted selection (MAS) of SPC towards accelerated breeding of improved cultivars, we have explored two diverse recombinant inbred line (RIL) populations to identify the quantitative trait loci (QTLs) associated with SPC. The two RIL populations, MP 1918 × P0540-91 (PR-30) and Ballet × Cameor (PR-31), were derived from crosses between moderate SPC × high SPC accessions. A total of 166 and 159 RILs of PR-30 and PR-31, respectively, were genotyped using an Axiom® 90K SNP array and 13.2K SNP arrays, respectively. The RILs were phenotyped in replicated trials in two and three locations of Saskatchewan, Canada in 2020 and 2021, respectively, for agronomic assessment and SPC. Using composite interval mapping, we identified three QTLs associated with SPC in PR-30 and five QTLs in PR-31, with the LOD value ranging from 3.0 to 11.0. A majority of these QTLs were unique to these populations compared to the previously known QTLs for SPC. The QTL SPC-Ps-5.1 overlapped with the earlier reported SPC associated QTL PC-QTL-3. Three QTLs, SPC-Ps-4.2, SPC-Ps-5.1, and SPC-Ps-7.2 with LOD scores of 7.2, 7.9, and 11.3, and which explained 14.5%, 11.6%, and 11.3% of the phenotypic variance, respectively, can be used for marker-assisted breeding to increase SPC in peas. Eight QTLs associated with the grain yield were identified with LOD scores ranging from 3.1 to 8.2. Two sets of QTLs, SPC-Ps-2.1 and GY-Ps-2.1, and SPC-Ps-5.1 and GY-Ps-5.3, shared the QTL/peak regions. Each set of QTLs contributed to either SPC or grain yield depending on which parent the QTL region is derived from, thus confirming that breeding for SPC should take into consideration the effects on grain yield.

Identification of DNA markers of high protein content in pea seeds as a promising raw material for food production

The paper presents data reflecting the process of certification of peas as a promising plant raw material for food production. The elements of the crop structure and determination of the protein content in pea seeds were analyzed. Sources for selection have been identified that are characterized by high protein content in seeds, early ripening, high productivity, and resistance to biotic and abiotic factors. It is noted that typing using microsatellite markers makes it possible to create databases for identifying varieties and lines. This leads to the planning of effective hybridizations. identifying the most significant genetic polymorphism. According to phenological observations, accounting of elements of the crop structure and analysis of protein content in seeds for 2022-2023. 22 sources of selection-valuable traits were identified.

Multi-environment testing for G×E interactions and identification of high-yielding, stable, medium-duration pigeonpea genotypes employing AMMI, GGE biplot, and YREM analyses.

Pigeonpea [Cajanus cajan (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (<180 days to maturity), which makes it essential for breeders to focus on the development of stable high-yielding varieties. The diverse agroecological regime in the Indian subcontinent necessitates an efficient multi-environment study by taking into consideration genotype (G) × environment (E) interaction (GEI) that has a significant impact on traits like grain yield (GY) in developing high-yielding and widely adaptable varieties. In the present study, 37 pigeonpea genotypes were evaluated during the 2021 rainy season at ARS Badnapur, ARS Tandur, BAU Ranchi, GKVK Bengaluru, and ICRISAT Patancheru. The GEI was significant on the grain yield (p < 0.01), and hence, genotype + genotype × environment (GGE) and additive main effects and multiplicative interaction (AMMI) biplots along with AMMI stability value (ASV) and yield relative to environmental maximum (YREM) statistics were used to identify stable high-yielding genotypes. The interaction principal component analysis 1 and 2 (IPC1 and IPC2) explained 40.6% and 23.3% variations, respectively. Based on the rankings of genotypes, G37 (ICPL 20205), G35 (ICPL 20203), G8 (ICPL 19404), G17 (ICPL 19415), and G9 (ICPL 19405) were identified as ideal genotypes. Discriminativeness vs. representativeness identified GKVK Bengaluru as an ideal environment for comprehensive evaluation of test genotypes. However, ICPL 19405 was identified as the potentially stable high-yielding genotype for further testing and release across the test environments based on its mean grain yield (1,469.30 kg/ha), least ASV (3.82), and low yield stability index (YSI) of 13.

A QTL allele from wild soybean enhances protein content without reducing the oil content

Abstract Soybean is one of the chief crops producing protein and oil for human consumption. Wild soybean, the ancestor of cultivated soybean, possesses high seed protein content; therefore, it is a valuable genetic resource that could enhance protein content in the cultivated varieties. To identify the genes responsible for increasing protein content in wild soybean, a population comprising 113 BC4F6 chromosome segment substitution lines (CSSL) was developed from a cross between soybean cultivar ‘Jackson’ and wild soybean accession JWS156-1. The CSSL population was cultivated in the field conditions for 3 years (2018, 2019 and 2020), and the seeds harvested from each line were analysed for protein and oil contents by InfraTec Nova instrument. Quantitative trait locus (QTL) analysis with 243 SSR markers identified 12 QTLs associated with seed protein, oil and protein + oil contents. Among these QTLs, qPro8 and qPro19, two major and stable QTLs for protein content, were detected on chromosomes 8 and 19, respectively. No QTL for oil content was detected in the vicinity of qPro19, indicating that qPro19 did not influence the seed oil content. The effect of qPro19 was validated using near-isogenic lines (NILs) of qPro19. By introducing the qPro19 allele from wild soybean into another soybean variety, ‘Tachiyutaka’, a BC4 line, T-678, that showed enhanced seed protein content, without reducing the seed oil content. This study implied that the qPro19 allele from wild soybean could be a potential genetic resource for breeding programmes aimed to improve soybean seed quality.

QTLs and Candidate Genes for Seed Protein Content in Two Recombinant Inbred Line Populations of Soybean.

This study aimed to discover the quantitative trait loci (QTL) associated with a high seed protein content in soybean and unravel the potential candidate genes. We developed two recombinant inbred line populations: YS and SI, by crossing Saedanbaek (high protein) with YS2035-B-91-1-B-1 (low protein) and Saedanbaek with Ilmi (low protein), respectively, and evaluated the protein content for three consecutive years. Using single-nucleotide polymorphism (SNP)-marker-based linkage maps, four QTLs were located on chromosomes 15, 18, and 20 with high logarithm of odds values (5.9-55.0), contributing 5.5-66.0% phenotypic variance. In all three experimental years, qPSD20-1 and qPSD20-2 were stable and identified in overlapping positions in the YS and SI populations, respectively. Additionally, novel QTLs were identified on chromosomes 15 and 18. Considering the allelic sequence variation between parental lines, 28 annotated genes related to soybean seed protein-including starch, lipid, and fatty acid biosynthesis-related genes-were identified within the QTL regions. These genes could potentially affect protein accumulation during seed development, as well as sucrose and oil metabolism. Overall, this study offers insights into the genetic mechanisms underlying a high soybean protein content. The identified potential candidate genes can aid marker-assisted selection for developing soybean lines with an increased protein content.

OAC Stirling soybean

OAC Stirling is a semi-determinate large-seeded food-grade soybean [ Glycine max (L.) Merr.] cultivar with high yield potential, high seed protein concentration, and resistance to soybean cyst nematode. OAC Stirling is developed and recommended for soybean growing areas in southwestern Ontario with 3100 or greater crop heat units and has a relative maturity of 2.3.

Exploring practical nutrition options for maximizing seed yield and protein concentration in soybean

Soybean seed yield and protein concentration are functions of the genetic, environmental, and management interactions. Both traits are frequently reported as negatively correlated since increases in seed yield are normally not accompanied by increases in seed protein concentration. The main goal of this study was to explore the impact of practical management decisions like (i) nutrition at sowing, and (ii) foliar fertilization at the beginning of seed-filling on seed yield and protein concentration of GMO and non-GMO soybean, across two regions with contrasting historical seed yield and protein concentrations: (i) high yielding and low protein region – Central region, and (ii) low yielding and high protein region – North-West region of Argentina. Within regions, fields were further classified as high and low productivity according to farmers' expertise. For Central region, improved nutrition at sowing yielded 5.4% (p < 0.05) and showed 0.9% more seed protein concentration than conventional treatment (p < 0.001). Non-GMO genotypes presented 9.6% less seed yield but 0,7% more seed protein concentration than GMO ones (p < 0.01). Seed yield differences between foliar treatments were variable between field productivities (p < 0.05) with no effects on seed protein concentration. High productivity fields showed ∼16% more seed yield and 0.7% higher seed protein concentration than low productivity fields (p < 0.001). For North-West region, seed yield was not affected by sowing treatments. Seed protein concentration of improved treatment was 0.3% higher than conventional treatment at sowing (p < 0.05). Likewise, non-GMO genotypes showed 0.7% more seed protein concentration than GMO ones (p < 0.001). Regardless of the region, changes in seed protein concentration due to the changes in tested factors were not correlated with changes in seed yield (p > 0.05). Our findings highlighted that (i) fertilization and inoculation constitute practical options for farmers to modify soybean productivity and seed quality, (ii) the effectiveness of changes in some input practices like foliar fertilization depended on the region where soybean was cultivated and (iii) not all changes in tested factors that allowed increased yields, impacted negatively on soybean seed protein concentration.

Seed Protein Genetics Linked with Nitrogen and Phosphorus Translocation Efficiency in Soybean

Soybean (Glycine max (L.) Merr.) is an important nutritional crop with high seed protein content. Production of high protein concentrations relies on sufficient nutrient supplies, especially of nitrogen (N) and phosphorus (P). Although the genetic basis for seed quality traits has been well studied, little information exists on any genetic connections between seed quality and nutrient supplies in soybean. Here, a recombinant inbred line (RIL) population of 179 progeny was generated using HC6 and JD17 as parents contrasting in seed quality and N and P translocation efficiencies. Seed protein and N and P translocation efficiencies were higher in HC6 than in JD17. Meanwhile, positive correlations were observed between seed protein content and translocation efficiency of N and P in RILs, implying that high N and P translocation efficiencies might facilitate seed protein accumulation. A genetic map was constructed using 5250 SNP markers covering a genetic distance of 3154.83 cM. A total of 6 loci for quality and 13 loci for N and P translocation efficiency were detected. Among them, two fragments on chromosome 6 and chromosome 20 contained multiple significant markers for both quality and N and P translocation efficiencies, with the respective observed LOD values ranging from 2.98 to 5.61, and 3.01 to 11.91, while the respective PVE values ranged from 8.2% to 13.9%, and 8.3% to 28.0%. Interestingly, one significant locus on chromosome 20 appears to be the product of a transposable element (TE) InDel in Glyma.20G085100, with progeny lacking the TE also exhibiting higher N and P translocation efficiencies, along with higher seed protein contents. Taken together, these results provide genetic evidence that increasing N and P translocation efficiencies may lead to increasing protein contents in soybean seeds. Furthermore, a TE InDel may be used as a genetic marker for breeding elite soybean cultivars with high protein content and N and P translocation efficiencies.

Bioenergy component of seeds and green mass of indian pea in various growing conditions

The article is devoted to the analysis of the results of studying cultivars of indian pea (Lathyrus sativus L.) under the conditions of the Nizhnevolzhsky (Saratov) and West Siberian (Omsk) regions of the Russian Federation. Indianpea serves a variety of purposes: compound feed, forage, an alternative source of protein for the population, due to the high protein content in seeds and mature leaves. In the arid conditions of the Russian Federation, the indianpea is able to occupy its own agronomic niche and become an additional source of replenishing cheap protein and improving the quality of the forage base. In this connection, a study of four varieties of the indianpea breeding of the 1Russian Research Institute for Sorghum and Maize "Rossorgo" (Racheika, Zhemchuzhina, Elena, Mramornya) was carried out according to economically valuable parameters, data were obtained on the biochemical composition of seeds and green mass, and an assessment was carried out on bioenergetic parameters. The influence of factors “A”, “B” and their interaction “AB” on the manifestation of signs was noted. According to the results of a two-factor analysis of variance, it follows that the factor of growing conditions (A) has the greatest influence on the seed yield index – 64.2%, and the yield of green mass - the genotype factor (B) - 59.5%. The study revealed a high protein content in seeds (28.3-31.1%) and green mass (21.0-26.3%). Determination of the biochemical composition makes it possible to evaluate varieties in terms of gross energy output with seeds and green mass. According to the parameters of the bioenergetic efficiency of seeds, the best indicators were found in the varieties Elena and Zhemchuzhina. Variety Elena was distinguished by the highest content of total energy in the green mass of 48.0-94.8 GJ/ha. Variety Zhemchuzhina was characterized by the maximum content of total energy in the grown seed crop – 26.0-29.3 GJ/ha, the largest increment in gross energy – 12.6 GJ/ha and the highest energy efficiency coefficient of all four varieties – 1.94.

Physiological and biochemical analysis of peanut seeds (Arachis hypogaea) after storage using silica gel

Peanuts contain high enough nutrients, especially protein and fat. The high fat and protein content in peanut seeds causes deteriorate faster during storage. Special treatment is required to reduce the deterioration. This study aimed to analyze the physiological and biochemical responses of peanut seeds after being stored using silica gel 120 days. This research used a completely randomized design and used the Pearson correlation test. Seeds were stored with different dosages of silica gels: control, 10gr, 20gr, and 30gr. The observed variables consisted of maximum growth potential, germination percentage, vigor index, relative growth speed, growth uniformity, seed water content, fat, and protein seeds. The results showed that storage using silica gel dosage had a significant response on maximum growth potential, germination percentage, vigor index, relative growth speed, and growth uniformity. Using silica gel treatment with a dose of 30g showed better seed viability and vigor values than other treatments. Based on the results of the correlation analysis of seed protein content, there was a strong correlation in the viability and vigor of seeds.

ПРОДУКТИВНОСТЬ И КАЧЕСТВО СОРТОВ СОИ ИЗ МИРОВОЙ КОЛЛЕКЦИИ В УСЛОВИЯХ СЕВЕРНОГО КАЗАХСТАНА

The article summarizes the results of a long-term study of soybean varieties from the world collection in the North of Kazakhstan, in the Kostanay region on the experimental fields of LLP "SKOS "Zarechnoye". In the period from 2020 to 2022, about 250 varieties of various ripeness groups from the world soybean collection were screened. An assessment of their ability to form a crop in the conditions of Northern Kazakhstan was carried out. Full filling of seeds during the years of research of the collection was achieved in most cases by cultivars created by breeders of the northern countries of the world, since they have low photoperiodic sensitivity, are not oppressed by low temperature, but at the same time are sensitive to excess precipitation. The protein content in the seeds of the studied soybean samples belonging to the precocious group varies from 37.8 to 41.1%. The highest indicator is in the Hay Hek variety 14 (41.1%). But among all the numbers of the collection, the leading indicators are: 126/1, 186/1, Zlata and Pitket K6298, respectively 44.2%, 44.3%, 44.5 and 45.4%. The high protein content in soybean seeds can be explained by their high ability to symbiotic nitrogen fixation. The studied gene pool has been characterized by a number of characteristics and can serve as a valuable source material for the creation of varieties recommended for cultivation in the North of Kazakhstan.

Prediction of Protein Content in Pea (Pisum sativum L.) Seeds Using Artificial Neural Networks

Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security.