Effective multidimensional treatment identification of different chemical fertilizers: Response of insect dynamics and rice production.

Effective management of fertilizers is essential in influencing the prevalence of insects in rice (Oryza sativa L.) fields. Over two years (2019-20 and 2020-21), an experiment conducted at Bangladesh Rice Research Institute (BRRI), Habiganj, during the boro season aimed to identify the most effective multidimensional treatment (EMT) by testing various combinations of chemical fertilizers and its effect on rice insects. The goal was to optimize rice grain yield while minimizing harmful insect infestation and supporting natural enemies. Eight different chemical fertilizer applications were used as follows: T1 contained a full mix of nitrogen (N), phosphorus (P), potassium (K), and sulfur (S); T2 had PKS but lacked N; T3 had NKS but lacked P; T4 had NPS but lacked K; T5 had NPK but lacked S; T6 had KS but lacked N and P; T7 had PS but lacked N and K; and T8 lacked all four elements - N, P, K, and S. The relationship between the dynamics of harmful insects and natural enemies was highly positively correlated (r = 0.72 to 0.97). In two consecutive growing years, the 2020-21 season exhibited notably higher counts of harmful insects, with Rice Leafroller (RLR) dominating in the booting stage and White Backed Planthopper (WBPH) in mid-tillering, while Green Mirid Bug (GMB) prevailed among natural enemies across both stages, surpassing insect pest counts, notably GMB, Lady bird beetle (LBB), Carabid beetle (CDB), and Staphylinid (STD). However, the yield was notably higher in the 2019-20 growing season despite these pest pressures. Throughout the mid-tillering and booting stages, T1 consistently exhibited the highest average populations of harmful insects and natural enemies, while T7 demonstrated the lowest count of harmful insects, followed by T2 at both growth stages. Additionally, the highest grain yield (GY) was consistently recorded in T1, followed by T5, T6, and T3, with yields of 7.98 t/ha, 7.63 t/ha, 7.38 t/ha, and 7.33 t/ha, respectively. In both stages, beneficial insects prevailed over harmful ones in all fertilizer applications, with significant declines noted in T2 and T7. Factor analysis showed successful selection for EMT in the MGIDI index for all variables except INT and GY during the 2019-20 season, with selection differentials (SD) ranging from -0.10 to 8.29. However, in 2020-21, selection was achieved for all variables with SD ranging from 0.37 to 6.08. According to the MGIDI index, the top-ranked EMTs were identified as T4 and T3 for the 2019-20 period, and T3 and T5 for the 2020-21 period. The EMT shared in both years, T3, proved effective because of its positive impact on enhancing natural enemies throughout both periods (with SD ranging from 4.76 to 8.29 for 2019-20 and 3.03 to 6.08 for 2020-21), and its notable contribution to rice grain yield (SD = 0.37) in 2020-21. This study uniquely integrates EMT to optimize rice grain yield while simultaneously managing harmful insect infestations and supporting natural enemies, addressing a critical need in sustainable rice cultivation. The suggestion is to give preference to fertilizer application T3, which omits P but contains N and K, to improve rice grain yield and boost natural enemies, thereby reducing harmful insect infestation. Moreover, future investigations should concentrate on refining fertilizer blends to strike a harmony between maximizing yield and fostering ecological robustness in rice cultivation.

Ideotype based genotype selection in a multivariate dataset of sweet potato (Ipomoea batatas L.).

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020-21 and 2021-22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

Sweet potato (Ipomoea batatas L.) genotype selection using advanced indices and statistical models: A multi-year approach.

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato.

Stability and performance analysis of storage root yield in a dataset of sweet potato varieties (Ipomoea batatas L.).

The dataset focuses on evaluating the performance of 17 sweet potato varieties (G) released by the Bangladesh Agricultural Research Institute (BARI) in terms of storage root yield and stability across five locations (E) in Bangladesh-Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The result revealed that BARI Mistialu-12 exhibited the highest average storage root yield at 45.35 t/ha, closely followed by BARI Mistialu-16 at 44.64 t/ha. Conversely, BARI Mistialu-1 had the lowest mean yield of 25.99 t/ha. Among the locations, Bogura recorded the highest mean root yield at 37.05 t/ha, while Chattogram exhibited the lowest at 31.27 t/ha. A combined analysis of variance revealed the presence of variability in storage root yield attributed to the genotype-location (environment) interaction (GEI). To delve deeper into this interaction, additive and multiplicative interaction effect models (AMMI) along with a linear mixed model (LMM) were employed for further investigations to confirm the significant contribution of GEI variance to root yield. The LMM results showed genetic variance (%), heritability (%), selection accuracy (%), and GEI correlation coefficients of 52.27, 54, 94, and 30, respectively. The AMMI analysis indicated that the first two principal components accounted for 74.60 % of GEI, with 20.16 % attributed to it. Assessing significant Interaction Principal Component Analyses (IPCAs) through the Weighted Average of Absolute Scores (WAAS) indicated that BARI Mistialu-12 is the most stable genotype, followed by BARI Mistialu-16 and BARI Mistialu-8, all displaying above-average root yield. The mega-environment analysis associated the highest root production of BARI Mistialu-11 and BARI Mistialu-2 with the Jamalpur location, while Gazipur, Bogura, and Jashore were linked with the superior performance of BARI Mistialu-12 and BARI Mistialu-16 genotypes. These findings are crucial for future breeding programs and the rapidly growing sweet potato industry, given the stable high-yield potential across diverse agro-ecological conditions. However, it is imperative to repeat the study to ensure reliable outcomes.

Dataset on utilizing cropping system-based fertilization techniques to improve soil health and crop output while minimizing tillage.

The dataset provided details on how tillage methods and nutrient management impacted the productivity of the four crops (mustard>mungbean>Transplanting (T.) aus >Transplanting (T.) aman) cropping system and the overall soil health. The specific tillage techniques examined were minimum tillage (MT), conventional tillage (CT), and deep tillage (DT). Regarding nutrient management, NM1 utilized 100 % soil test-based (STB) fertilization following fertilizer gradient generation (FRG); NM2 applied 125 % of STB after FRG-2018; NM3 consisted of 100 % STB (with 80 % from chemical fertilizers and 20 % from cow dung); and NM4 relied on native fertility without any fertilization. Over three consecutive seasonal years (2018-19, 2019-20, and 2020-21), twelve treatments were replicated three times following a factorial totally randomized design. The comparative analysis of crop yield, rice equivalent yield, system productivity and production efficiency indicated superior performance of MT over both CT and DT. Furthermore, in relation to agricultural productivity metrics, the application of the nutrition package NM3 demonstrated performance levels exceeding the average. The adoption of MT and the incorporation of the NM3 nutrition package led to notable advancements in organic matter, field capacity, microbial biomass nitrogen, microbial biomass carbon and soil nutrient levels (N, P, K, S, Zn, and B). Consequently, the synthesis of the NM3 with MT is posited as a strategic approach for soil enhancement and the augmentation of crop productivity.

Genotypic selection and trait variation in sweet orange (Citrus sinensis L. Osbeck) dataset of Bangladesh.

The dataset primarily focused on selecting genotypes of sweet oranges based on their phenotypic performances. The dataset resulted significant variations in the best linear unbiased predictions (BLUPs) of 20 out of 21 traits, including leaves, flowers, fruits, and seeds. A strong positive correlation (r= 0.73 to 0.95) was observed among the majority of morphological traits. The sweet orange genotypes demonstrated considerable genetic variance, surpassing 65% for almost all traits, with a selection accuracy exceeding 92%. Using the multi-trait genotype-ideotype distance index (MGIDI), CS Jain-001 emerged as the top-ranked genotype, followed by BAU Malta-3 and CS Jain-002 in order of desirability. The broad sense heritability of selected traits was above 75.60%, and the selection gain reached a maximum of 12.60. These identified genotypes show promise as potential parent donors in breeding programs, leveraging their strengths and weaknesses to develop promising varieties in Bangladesh.

Selection of waterlogging tolerant sesame genotypes (Sesamum indicum L.) from a dataset using the MGIDI index.

The dataset explores the impact of waterlogging stress on sesame plants during the pre-flowering stage, recognizing its global impact on crop yield and the identification of tolerant genotypes using the MGIDI index. Carried out in Bangladesh, the research assesses the survival status, grain yield, and stress tolerance indices of 40 sesame genotypes, revealing that twelve of them demonstrated resilience under 72 h of waterlogging stress at the pre-flowering stage. There were variations in genotypic grain yield, and G15 exhibited the highest yields, recording 5.22 g/plant under normal conditions and 4.10 g/plant under waterlogging stress. The MGIDI index, evaluating waterlogging tolerance, identified G4 as the most favorable genotype, followed by G5 and G12. Factor analysis within the MGIDI index uncovered distinct tolerance and susceptibility indices, highlighting strengths and weaknesses in the selected genotypes. The selection gain percentages of these genotypes ranged from 12.9 to 37.4, indicating high broad-sense heritability (≥0.97). These results underscore the potential of genotype selection based on waterlogging stress indices, providing valuable insights for breeders addressing stress-related crop challenges in the face of changing climatic conditions.

Multi trait stability indexing and trait correlation from a dataset of sweet potato (Ipomoea batatas L.).

A study was conducted in five regions of Bangladesh, specifically Gazipur, Bogura, Jamalpur, Jashore, and Chattogram, each characterized by suitable agro-ecologies for sweet potato cultivation. The purpose of this data article was to demonstrate the correlations between traits and the selection of stable varieties based on the multi-trait stability index (MTSI). The data indicated a direct link between multiple characteristics and both the yield and factors contributing to yield. This implies that enhancing these traits might result in a higher overall production of sweet potato storage roots. Furthermore, the factor analysis for MTSI demonstrated that the desired goal for selection was achieved for all traits, except for mean vine length (VL) and storage root dry weight (DW). The broad sense heritability ranged from 0 to 0.97, and the selection gain percentage ranged from 0 to 42.8. The MTSI analysis identified the sweet potato variety BARI Mistialu-15 as the most stable among the other studied varieties.

Cropping system-based fertilizer strategies for crop productivity and soil health under minimum tillage in grey terrace soil.

A cropping system that is based on three or four crops is currently a widely favored option for augmenting crop productivity to address the escalating global food demand. However, the improper fertilizer management and undue tillage adversely impacts both the productivity of crops and the fertility of the soil. A research investigation was conducted on tillage and nutrient management within the mustard-mungbean-Transplanting aus (T.aus)-Transplanting aman (T.aman) cropping system to examine the impact of fertilizer packages and tillage techniques on the overall productivity of cropping systems, as well as the condition of the soil in grey terrace soil. The research included tillage techniques viz; minimum tillage (MT), conventional tillage (CT) and deep tillage (DT); while nutrient management; NM1: 100 % STB (Soil test based) following FRG (Fertilizer Recommendation Guide-2018), all from chemical fertilizer, NM2: 125 % of STB following FRG- 2018, all from chemical fertilizer, NM3: 100 % STB (80 % from chemical fertilizers and 20 % from cowdung), and NM4: Native fertility (no fertilization). A total of twelve treatments replicated three times following the factorial completely randomized design for three consecutive seasonal years (2018-19, 2019-20, and 2020-21). MT outperformed DT and CT in terms of crop yield, rice equivalent yield (REY), system productivity (SP), and production efficiency (PE). Moreover, NM3 exhibited enhanced performance in terms of agricultural productivity measures. Field capacity (FC), soil organic matter (OM), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and soil nutrients (N, P, K, S, Zn and B) observed an enhancement as a result of the implementation of tillage MT and nutrition package NM3. The investigation indicates that implementing minimum tillage (MT) coupled with an integrated plant nutrition system package (NM3) can assist in the improvement of soil and the enhancement of crop productivity.

Yield performance and trait correlation of BARI released sweet potato varieties studied under several districts of Bangladesh.

A study was carried out in five sweet potato growing regions of Bangladesh, each characterized by suitable agro-ecologies, in order to demonstrate the most favorable varietal performance and trait correlations. A completely randomized block design with three replications was used to compare the varietal performance of BARI (Bangladesh Agricultural Research Institute) released sweet potato varieties (viz. BARI Mistialu-9, BARI Mistialu-10, BARI Mistialu-12, BARI Mistialu-15 and BARI Mistialu-17). During the 2021-22 cropping season, sweet potato varieties were tested in five districts of Bangladesh, namely Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The findings revealed that the BARI Mistialu-12 variety exhibited remarkable attributes, including a high marketable storage root yield of 39.88 t/ha. Additionally, it demonstrated exceptional performance in various yield components such as vine length, average storage root weight, and dry weight of the root. Furthermore, a positive correlation was observed between several traits and yield, as well as yield-attributing characteristics. This correlation suggests that enhancing these traits could potentially contribute to an overall increase in the storage root yield of sweet potatoes.