Agricultural Research最新文献

Hull-less seed trait is readily available in Cucurbita pepo, but to enhance its yield in diverse climatic conditions transfer of this trait in another related species, viz. C. moschata, is highly desirable. Consequently, a set of total 33 hull-less seeded genotypes including 31 derived from interspecific hybridization between C. pepo (hull-less) and C. moschata (hulled), and two C. pepo genotypes were evaluated for various fruit and seed traits. Analysis of variance revealed significant variation among the genotypes for fruit (fruit weight, polar and equatorial diameter) and seed (seed length, width, thickness, number of seeds, and 100-seed weight) traits. An interspecific hybridization-derived genotype, viz. HL3643-6, was comparable to check C. pepo genotypes for most of the traits. All studied traits exhibited high heritability and genetic advance, with a significant positive correlation confirming their interrelationship. Principal component analysis indicated that the first two components contributed to 82.66% of the cumulative variance, with all eight traits contributing to PC1 and seed thickness along with hundred-seed weight being the major contributors to PC2. Overall, the investigation highlighted substantial variation in the interspecific derived lines and their potential for exploitation in improving the hull-less seed trait.

With the progress in sensor and cloud technologies in contemporary times, a range of intelligent agriculture applications has gained considerable prominence. It is predicted that these developments can continue to pique the interest of researchers in the future. On the other hand, it is seen that IoT (Internet of Things)-based models are used in various fields. Herein, the primary objectives of this study are to enable farmers to remotely monitor and manage field conditions through sensor technology and IoT integration. In addition, these technological advancements make it possible to take the required measurements. Farmers can optimize their agricultural practices based on the analysis of the data obtained for this application. Thus, the aim is to manage the agricultural process more effectively and efficiently. In this study, an IoT-based framework is proposed for agricultural data monitoring. Light, temperature–pressure, smoke, humidity, and soil dryness values can be measured from GY-30, BME280, MQ-2, DHT11, and YL-69, respectively. An ESP-32S development board is used to collect data from sensors, and this board is coded using Arduino IDE. Subsequently, using ESP-32S, it is sent to the ThingSpeak cloud service provided by MATLAB via a Wi-Fi connection. Thus, these data can be easily transferred to MATLAB. We create a user-friendly Graphical User Interface application so that the data can be monitored and analyzed in MATLAB as well as ThingSpeak. This application allows users to monitor the data flow in real time and can easily provide the requested values such as maximum, minimum, mean, standard deviation, and current with the help of a button. In addition, the proposed system sends an e-mail to the user when soil dryness and smoke values exceed a certain threshold value. The results obtained in the study indicate that the proposed model can save time and labor in addition to providing reliable and fast data flow.

Poor farming practices have contributed to the degradation of the majority of soils in Burkina Faso. This has led to the formation of bare soils and new clearings. These soils are generally reclaimed by zaï, an ancestral water and soil conservation practice that needs further improvement to ensure food security. The following study consisted of giving a rectangular shape to the usually spherical zaï pits in order to measure sorghum yield’s parameters in a factorial design: type of amendments (compost + NPK and compost + urea + Burkina phosphate); length (35 and 45 cm) and depth of the pits (10 and 20 cm) with 4 replications. Using the sites of Kiembara (Sahelian zone) and Arbollé (Southern Sudan-Sahelian zone), sorghum growth, biomass, grain yield, harvest index and threshing rate were measured. Overall, highlight the importance of rainwater harvesting using zaï pits and the use of manure in combination with mineral fertilizer supplements in improving soil fertility and enhancing crop yields. There is good evidence that grain yield was significantly improved under rectangular zaï with 45-cm-long pits (2032.7 kg.ha⁻¹ in Kiembara, 1561.7 kg.ha⁻¹ in Arbollé) than under ordinary zaï (1053.6 kg.ha⁻¹ in Kiembara; 990.2 kg.ha⁻¹ at Arbollé). This yield improved further with the application of compost combined with Burkina phosphate and urea under the rectangular zaï of 45-cm-long and 20-cm deep pits, 2333.9 kg.ha⁻¹ in Kiembara and 1967.7 kg.ha⁻¹ in Arbollé. Under ordinary zaï, the same inputs resulted in a grain yield of around 1034.5 kg.ha⁻¹ in Kiembara and 916 kg.ha⁻¹ in Arbollé. Hence, rectangular zaï was better than ordinary zaï in increasing the agronomic parameters of sorghum, especially in the Sahelian agroclimatic zone, providing good water conditions to produce enough useful cobs and therefore a solution to reduce food insecurity.

Growth analysis is a valuable method for quantitatively investigating the growth and development of products. To analyze plant growth during the growing season, access to accurate and regular plant information is needed, which is obtained by measuring leaf surface and dry matter accumulation. The use of nonlinear regression models is expanding due to having parameters with physiological meaning in growth analysis. Of these models, there are beta, logistic, Gomperts, Richards, linear, cut and symmetric linear models. Therefore, this study was conducted on bean plant of the variety “Barakt” under factorial experiment in the form of basic randomized complete block design with four crop densities in four replications under rainfed conditions at the research farm of Gorgan University of Agricultural Sciences and Natural Resources in 2014–2015, located in the west of Gorgan, with a latitude of 37° and 45 min north and a longitude of 54° and 30 min east and an altitude of 120 m above sea level. In this study, the nonlinear beta and logistic regression models were fitted to leaf surface data, and beta, Gompertz and logistic models were fitted to bean dry weight. The AICc criterion analysis showed that the beta model had a better fit than the logistic model for leaf area. According to this model under various crop densities, LAI_max was between 2.30 and 5.30 g per square meter, t_m was from 131.90 to 144.20 days after planting, and t_e was between 158.7 and 163.50 days. Also, the analysis of the AICc criterion for dry matter accumulation showed that the beta model was better in fitting the dry matter accumulation than Gomperts and logistic models. According to this model, W_max varied between 1.725 and 1484.3 g per square meter, t_m between 138.30 and 146.40 days after planting, and t_e between 162.60 and 179.0 days in different densities.

Agronomic practices related to post-harvest losses pose a serious threat to onion cultivation, especially in the area of nutrient management in Ethiopia. A study was conducted to evaluate the effect of nitrogen (N) and sulfur (S) fertilizer levels on the storage potential and quality of onion bulbs during 2021 and 2022 growing seasons. Two sets of experiments were carried out. The first experiment was used to produce onion bulbs with different levels of N and S fertilizers. The second one was designed on the first experiment using a completely randomized design (CRD) with three replications. Total weight loss, weight loss causal factors, and total soluble solids (TSSs) were recorded for different durations. The application rate of nitrogen fertilizer increased during bulb production, resulting in a significant increase in the mean stored bulb weight loss after 30 to 90 days of storage. On the other hand, using S fertilizer during the dry-hot season lowers the post-harvest loss of onion bulbs by approximately 5.89% compared to not applying it. In both seasons of storage, bulb TSS was increased by the combination of S and N fertilizer applications. Overall results revealed that applying 92 kg of N and 20 kg of S per hectare extended storage life, maintained quality, and was economical for storing onion bulbs for up to 60 days following harvest in the study area and similar environments.

Early blight is a severe disease which affects several plant species, including tomato plants. Weather parameters such as temperature, leaf wetness, soil moisture, and relative humidity play a vital role in the growth of diseases in plants. The current study analyses the effect of weather parameters on the development of early blight disease in tomato plants by utilizing traditional machine learning techniques. A real-time dataset TomEBD, comprising five weather parameters, has been employed. Three resampling techniques—Synthetic Minority Oversampling Technique(SMOTE), K-Means SMOTE(KM-SMOTE) and Support Vector Machine SMOTE(SVM-SMOTE)—have been used to balance the dataset. Five different machine learning classifiers—k-Nearest Neighbor(kNN), Support Vector Machine(SVM), Random Forest(RF), Artificial Neural Network(ANN), and Kernel Extreme Learning Machine(KELM)—have been used to classify a plant as healthy or diseased based on meteorological factors. The five classifiers are used on the imbalanced and three balanced datasets, resulting in 20 models. Hyperparameter tuning of all five classifiers has been done for optimization. The results indicate that out of the 20 models evaluated, the proposed model KELM-KM - KELM classifier on KM-SMOTE balanced data outperforms all others with a mean accuracy of 85.82%. A comparison with the existing studies shows that KELM-KM outperforms the state of the art without involving any complex feature extraction techniques. Therefore, it can be used to alarm the farmers for fungicide spray on diseased plants in conducive environments.

Sesame is an important oilseed crop, and the crop yields frequently fluctuate as the crop is largely grown in rainfed and low-fertile lands. Limited water availability negatively affects many physiological processes and the final productivity of sesame. Limited work has been carried out in the past to understand the role of plant growth regulators (PGRs) in modulating sesame growth and development for optimum productivity. A field study was conducted under rainfed conditions to evaluate the response of foliar application of different PGRs such as hormonal-based gibberellic acid (20 ppm); chemical-based thiourea (500 ppm); chemical-constituting structural component-based ortho-silicic acid (380 ppm); and control (water-sprayed) on sesame cultivars: Swetha til, GT-10, TKG-22, and JCSDT-26. The PGRs foliar application was done at the vegetative (25–30 days after sowing), 50% flowering (40–45 days after sowing), and seed development (70–75 days after sowing) stages of the crop. The results revealed that application of different PGRs positively influenced the plant’s growth, physiological, yield and quality traits; however, most effective results were obtained with gibberellic acid (20 ppm), followed by ortho-silicic acid (380 ppm), and thiourea (500 ppm) improved the morphological, yield, and yield-attributing traits. The interaction between PGRs and varieties was found significant and among the sesame cultivars, swetha til followed by JCSDT-29 was found most promising. The application of PGRs has significantly improved the plant height, leaf area, number of branches, capsules, seeds/capsules, seed yield oil content, and fatty acid content compared to the control by gibberellic acid, followed by ortho-silicic acid and thiourea. The interaction between PGRs and varieties was found to be significant, and cultivar Swetha til, a white-colored cultivar performed most superiorly among the different tested cultivars in terms of growth, physiology, yield as well and quality traits when treated with GA3 at 20 ppm. The seed yield was enhanced by 25–26%, 11–12%, and 6–7% with the application of gibberellic acid, ortho-silicic acid, and thiourea, respectively, over control. Considering the findings, it can be concluded that the application of PGRs (thiourea, ortho-silicic acid, and gibberellic acid) significantly enhanced the growth, physiology, yield, and quality of sesame under rainfed conditions; however, GA₃ at 20 ppm was found most effective and may not only enhance the optimum productivity but also effective in improving the quality traits of sesame.

The objective of this experiment was to assess the impact of different planting patterns on light availability for cowpea within the intercrop canopy, as well as to evaluate the corresponding effects on cowpea's physiological characteristics and grain yield in maize/cowpea intercropping systems. The experiment utilized a randomized complete block design with four replicates and included six treatments: sole cowpea with 40 kg N ha⁻¹ (T1) and 80 kg N ha⁻¹ (T2), simultaneous sowing of maize and cowpea with 40 kg N ha⁻¹ (T3) and 80 kg N ha⁻¹ (T4) and cowpea sown 3 weeks after maize with 40 kg N ha⁻¹ (T5) and 80 kg N ha⁻¹ (T6). Results showed that sole cowpea had higher light interception and leaf area index than intercropped maize/cowpea, regardless of sowing date and nitrogen supply. The highest photosynthetic rates of cowpea were observed in the intercropping maize/cowpea sown simultaneously with 40 kg N ha⁻¹ and sole cowpea with 80 kg N ha⁻¹. The water use efficiency of cowpea was found to be higher when intercropped with simultaneously sown maize, regardless of nitrogen supply, with values of 3.23 μmol CO₂/mmol H₂O and 3.3 μmol CO₂/mmol H₂O for treatments T3 and T4, respectively. Moreover, the highest cowpea grain yield was observed when maize and cowpea were sown simultaneously with the application of 80 kg N ha⁻¹ (0.99 t ha⁻¹), while the lowest yield was obtained when cowpea was sown 3 weeks after maize (0.37 t ha⁻¹), irrespective of nitrogen supply. Overall, our findings suggest that maize/cowpea intercropping with simultaneous sowing of maize can enhance the grain yield of cowpea in the Yucatan Peninsula.

Common bean (Phaseolus vulgaris L.) stands as the main leguminous crop cultivated in South-Kivu Province, DR Congo. However, there is a scarcity of information regarding the soil mycorrhizal potential, natural mycorrhization, and the spore density of arbuscular mycorrhizal fungi (AMF) associated with P. vulgaris in South-Kivu Province. Sample of rhizospheric soil and roots were collected from famer’s field in three localities in two territories namely Kabare and Walungu. The goal was to determine the natural mycorrhization rate of common beans, AM fungi spore density, and the soil mycorrhizal potential in relation with soil chemical properties. Our findings revealed that bean root colonization was notably high in Katana, Kavumu and Miti, while being comparatively low in Lurhala, Walungu centre, and Nduba. Katana and Kavumu exhibited a high number of spores (242.9 ± 37.8 and 183 ± 13.1 spores.100 g⁻¹ soil, respectively) compared to other sites. Soil mycorrhizal potential was higher in Katana, Kavumu, and Miti sites, located in Kabare territory (10.8 ± 0.7, 9 ± 1.3 and 8.8 ± 0.9 AM fungi propagula g⁻¹ soil). The AM fungi potential was positively and significantly correlated with bean mycorrhizal colonization (p < 0.0001). As soil phosphorus (P) content increased, mycorrhization frequency and intensity significantly decreased (r = − 0.69, p < 0.0001 and r = − 0.54, p = 0.002, respectively), along with the soil mycorrhizal potential (r = -0.87, p < 0.0001), regardless the study sites. Conversely, soil pH positively influenced mycorrhizal colonization (r = 0.73, p < 0.0001 and r = 0.54, p = 0.002, respectively), and the soil mycorrhizal potential (r = 0.78, p < 0.0001). This study underscores a substantial variation in common bean mycorrhizal status and soil mycorrhizal potential across sites. To enhance common bean productivity, it is recommended to consider site-specific identification of AM fungi morphotypes and inoculum production.