Agricultural Research最新文献

Quantifying and analyzing nitrous oxide (N₂O) emissions from sugarcane-cultivated soils is a priority issue due to its potential role in climate change in the coming decades. However, understanding the impact of this crop on global N₂O emissions is complicated by the variety of experimental conditions and methods used to quantify these emissions. This study aimed to determine the influence of experimental conditions and methodological approaches on quantifying of N₂O emissions in soils used for sugarcane production. For this purpose, a meta-analysis of quantitative information on this topic, and published-online up to December 2020 was performed. The average daily N₂O emission rate calculated in this research was higher than similar studies and the differences were attributed to methodological differences with these references. In addition, results showed that variables associated with sampling and gas concentration measurement had no significant effects on daily mean N₂O emissions. The evaluation period, soil texture, and the management of synthetic N sources and application rates were the main variables affecting N₂O emissions. Among the important sugarcane-producing countries United States and India had significantly higher daily mean N₂O emission (4.5 ± 1.4 and 3.08 ± 0.08 mg N₂O–N m⁻² d⁻¹, respectively) than the global average (1.98 ± 0.4 mg N₂O–N m⁻² d⁻¹). High N₂O emissions were widely documented in coarse or intermediate-texture soils. It is recommended that to reduce the uncertainty associated with the estimation of cumulative N₂O emissions the monitoring of N₂O emissions should include multiple complete growing seasons and include high sampling frequency around the main management practices. The strategies to reduce emissions should focus on the exploration of alternative N fertilizers to urea in sub-traditional doses for sugarcane areas with coarser textured soils. This work provides an important reference framework for the design and development of future research focused on the assessment of N₂O mitigation options for sugarcane.

In this study, the newly identified farmer pomegranate variety ‘Sharad King’ has been characterized for 48 traits including morphological and physico-chemical parameters to analyse its suitability for commercial cultivation and exportability. The variety has been evaluated for four consecutive years during Ambe bahar, and their mean performance was compared with the ruling variety ‘Bhagawa’. Two sample T-test analysis exhibited the presence of significant differences for twenty quantitative traits. The study showed that ‘Sharad King’ variety has better fruit size (299.79 g), red thick rind (4.65 mm), more number of arils per fruit (704.75), sweet red arils, soft seeds, matures in about 160–170 days after anthesis and better shelf life with comparatively lesser physiological loss in weight of fruits (2.22%) than the check variety ‘Bhagawa’. Rind per cent is comparatively 3.18% higher in ‘Sharad King’. Thus, the new variety ‘Sharad King’ was found to be promising for fresh consumption, processing purposes, and long-distance marketing. Cultivation of this new variety will help to overcome the drawbacks in the existing ruling variety ‘Bhagawa’ faced by the pomegranate growers.

As an important reference for assessing plant water consumption and estimating plant transpiration, it is of great significance to achieve accurate prediction of plant sap flow. A number of deep learning models were established and compared using approximately 3 years of continuous eucalyptus flow time series data collected from the SAPFLUXNET open dataset and 6 environmental factors, including shortwave solar incident radiation, air temperature, air relative humidity, net radiation, vapor pressure deficit, and photosynthetic photon flux density. The experimental results show that the improved Transformer model, with the introduction of a two-step self-attention mechanism and simplified design, maintains significant predictive performance advantages compared to the original Transformer model, long short-term memory, gated recurrent unit, and temporal convolutional neural network models. In the shorter 1-h forecast, the mean squared error and coefficient of determination (R²) of the improved Transformer model are 0.0191 and 0.965, respectively. Compared to the suboptimal typical Transformer model, the MSE is reduced by 22.9%, and R² is increased by 1.0%. Additionally, the improved model maintains stable predictive performance advantages in long-term plant flow prediction. In the longest 8-h advance prediction, the MSE is reduced by 14.9% compared to the suboptimal Transformer model, and R² increases by 3.0% compared to the Transformer model. The comprehensive experimental results show that the improved Transformer model makes more effective use of environmental information to achieve more accurate and long-term plant flow prediction. This study emphasizes the basic principle and validity of the two-step self-attention network structure and provides a valuable basis for developing more effective methods for predicting plant sap flow.

Bioactive compound ‘lutein’, an α-carotenoid is responsible for ocular health and preventing major cardiovascular diseases in human beings and it cannot be synthesized de novo. ‘Lutein content is found only in traces among the popular cultivars of rice. ‘Kavuni’, an Indian traditional landrace, rich in lutein content (238 μg/100 g) is used in the present study to transfer lutein into two popular cultivars ‘ASD 16’ and ‘Swarna Sub1’ using marker assisted backcross breeding strategy. Phenotyping for lutein quantification was done using HPLC (High performance Liquid Chromatography) and further antioxidant analysis was carried out by DPPH (1, 1-diphenyl-2-picryl-hydrazyl) based scavenging assay. Across the crosses, eight backcross progeny were found to have higher lutein content than respective recurrent parents with an average lutein content of 96.5 μg/100 g and background recovery of 64.53%. One progeny (S33) from Cross II recorded highest lutein content of 322 μg/100 g, much higher than the donor parent indicating transgressive segregation. Significant positive correlation was observed between lutein content and antioxidants among the backcross progeny.

Peppermint (Mentha × piperita) stands as the predominant mint species cultivated across various global regions, owing to its superior quality and abundance of essential oil. The objective of this study was to investigate the impact of distinct biological fertilizers on physiological and morphological traits of peppermint. An experiment was conducted employing a split-plot design arranged within a randomized block framework. The experimental treatments encompassed varying irrigation intensities (full irrigation, and moderate and severe water-deficit stress, irrigation at 75%, 60%, and 45% of the field capacity, respectively), along with six combinations of phosphorus chemical fertilizers and arbuscular mycorrhizal fungi (Glomus intraradices). Inoculation with mycorrhiza exhibited a significant effect on leaf dry weight and essential oil yield, resulting in an augmentation of 5.3% and 10.7%, respectively, when contrasted with the non-fertilizer treatment. The results obtained show that moderate water stress elicited the highest essential oil content at 2.2%. The treatment combining 50% chemical phosphorus fertilizer with mycorrhiza inoculation yielded the highest leaf phosphorus content at 0.41%, while the lowest content of 0.21% was observed in the non-fertilizer treatment. Water restriction led to a reduction in gas exchange parameters; nevertheless, the application of fertilizers improved these attributes across all irrigation regimes. The highest proline content (0.036 μg g⁻¹ fresh weight) was detected in non-fertilizer peppermint subjected to severe water stress. The study unveiled that the combined application of phosphorus chemical fertilizer and biological fertilizer yielded superior outcomes compared to individual applications, even under stressful conditions. This approach holds promise for achieving heightened productivity while mitigating excessive use of phosphorus chemical fertilizers, thereby curbing environmental pollution and advancing toward sustainable agricultural practices.

A wet laboratory experiment was carried out to evaluate the effect of Chanos chanos (Forsskal, 1775) produced greenwater under different salinities and stocking densities on growth performance, water characteristics, and microbial load. The experiment followed a 3 × 4 factorial design with three different salinities (10, 20, and 30 ppt), each with four different stocking densities (20, 40, 60, and 80 fish/tank). The C. chanos fingerlings (1.996 ± 0.14 g) were acclimatized to different salinities and distributed to 12 treatment groups in triplicates based on different stocking densities in a 500-L tank. At the end of the experiment, the average final weight, specific growth rate, FER, and PER were higher and FCR was better at 20 ppt salinity and stocking density of 20 fish/tank. Survival was not affected by the varied salinities and stocking densities. Greenwater parameters such as ammonia and nitrite values were significantly lower at 20 ppt salinity and at 20 fish/tank. Ammonia values were not significantly affected by the interaction of different salinities and stocking densities, whereas nitrite is significantly affected by the interaction in greenwater culture system. The total heterotrophic bacterial (THB) count in the culture water was lower at 20ppt salinity and 20 fish/tank, whereas the THB count of the C. chanos mucus sample was found to be at moderate count depicting less stress when fish were reared at salinity 20 ppt and 20 fish/tank. The algal density increased significantly with an increase in salinity and stocking density. To study the common bacterial diversity in the greenwater, metagenomics study of fish skin mucus was performed which provide baseline information about the potential beneficial microbial community that improves and maintains the culture environment and animal health. Overall, 20 ppt salinity and 20 fish/tank provided a suitable condition for culture without water exchange in tanks with beneficial mucus bacterial community. This study has potential application in brackishwater and inland saline aquaculture as these euryhaline fish species can be promoted in inland saline areas as climate-resilient adaptation strategies.

High-temperature stress poses a serious threat to groundnut production in semi-arid tropical regions due to climate change and global warming. It is important to develop tolerant cultivars that can adapt and produce higher yields. Thirty-six groundnut genotypes were evaluated for tolerance to high temperatures at the seedling stage using the Temperature Induction Response technique, followed by comparing seedling responses to yield performance under field conditions. This aids in understanding genotype responses to high-temperature stress at various growth stages and the possibility of early selection to accelerate breeding for high-temperature tolerance. In the TIR experiment, variability in seedling survival and growth reduction was observed, and the induced genotypes exhibited higher seedling survival and lower growth reduction compared to the non-induced genotypes. Field screening revealed significant genotype, environment, and genotype×environment differences for pod yield and associated traits under high-temperature stress. Heat-tolerant genotypes recorded higher pod yield and associated traits than sensitive genotypes. However, genotypes with seedling tolerance did not exhibit superior pod yields under high-temperature stress, which implies distinct mechanisms governing high-temperature tolerance at different growth stages. This recommends comprehensive screening of genotypes under high-temperature stress for future research and genetic improvement of groundnut high-temperature tolerance.

Accurate estimation of rice yield helps farmers in optimizing the risk and agronomic management practices. Conventional methods for assessing yield are time-consuming, labour-intensive and costly. Multispectral imageries captured by sensors mounted on unmanned aerial vehicles (UAVs) offer a cost-effective and efficient alternative. This study aimed to develop rice yield predictive machine learning models using vegetation indices (VIs) derived from multispectral imagery. The experiment was conducted in eastern region of India, with different rice cultivars and nitrogen levels to create the yield variability. Ten VIs were generated using imagery captured during panicle initiation stage of rice. Using variance inflation factor (VIF) technique, four VIs were selected for model building. Three models: random forest (RF), support vector machine (SVM) and artificial neural network (ANN) were built using rice yield as the target variable and vegetation indices (VIs) as predictors. The accuracy of SVM in predicting rice yield was higher as compared to RF and ANN. The predicted yield (t/ha) in the SVM model ranged from 3.73 to 5.45 (R² = 0.62), while it ranged from 3.83 to 5.00 for RF model (R² = 0.57) and 3.46 to 5.91 for ANN model (R² = 0.54). Notably, the normalized difference vegetation index and transformed chlorophyll absorption reflectance index were identified as significant contributors for model building. These findings demonstrate that the proposed approach can improve the prediction accuracy of rice grain yield.

Protoporphyrinogen oxidase (PPO) inhibitors are an effective class of herbicides with broad-spectrum applications. Recently, auspicious diaryl ether derivatives have been developed as PPO inhibitors and, herein, we report the optimization of these compounds guided by in silico approaches. First, the herbicidal activities of 28 substituted diaryl ethers were modeled using multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR), resulting in a reliable and highly predictive model (r² = 0.97, q² = 0.84, and r²_pred = 0.96). The model regression parameters were then applied to predict the bioactivities of proposed derivatives, whose design was oriented by MIA plots of variable importance in projection (VIP) scores and PLS regression coefficients (b); these contour maps give insight into how much and how (decreasing or increasing) the substituents affect the biological activities, respectively. Finally, docking studies and molecular dynamics validated the MIA-QSAR predictions and explained the ligand–enzyme interactions responsible for the predicted effects. Overall, a proposed fluorinated derivative demonstrated higher potential than the library compounds, and a possible synthetic route was suggested to obtain it.

Metering device is a main component of vegetable transplanters that could save cost of operation and labour requirement in transplanting. Therefore, a tractor-drawn three-row automatic vegetable transplanter using an inclined magazine-type metering device for cylindrical paper pot seedlings was developed and evaluated in field. Experiments on metering device were conducted at seven forward speeds 1.0, 1.2, 1.4, 1.6, 1.8, 2.0 and 2.2 km/h, to determine the optimal performance speed for 45-day old tomato seedlings. Data on seedling spacing, tilted planting, soil cover, seedling damaged while conveying and feeding and transplanting were recorded and analysed for conveying efficiency (CE), feeding efficiency (FE), transplanting efficiency (TE), overall efficiency (OE) and seedling spacing (SS). The CE, FE, TE and OE were found to be 100, 83.3, 91.7 and 96.7%, respectively, at 1.2 km/h. The SS was ranged from 633 to 651 mm for speed range of 1–1.2 km/h. Based on the optimized values of laboratory studies, a tractor-drawn three-row automatic vegetable transplanter was developed and evaluated in the field. The field performance data revealed that actual field capacity of the machine was 0.11 ha/h at a forward speed of 1.2 km/h, with a 50% field efficiency. The transplanter can transplant per row 33 seedlings/min, compared to 3.7 seedlings/min by manual method. Also, the saving in cost and labour is about 55 and 93.9% as compared to manual method. This transplanter offers efficient transplanting of potted seedlings, ensuring timely operation, labour savings and reduced drudgery compared to conventional practices.