Agricultural Research最新文献

In this paper, we propose a machine-learning model for predicting the shell weight of silkworm cocoons Antheraea mylitta D. (Saturnidae) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-invasive easy-to-measure cocoon features. We evaluate our model on four datasets from different families of cocoons. The evaluation shows that the proposed model accurately predicts the shell weight and outperforms well-known models, including neural network-based regression.

India’s growth story is not in consonance with that of the development. Agriculture is non-remunerative in India, and the situation turns out to be lot grimmer if we take into account the varying degree of land areas. Rainfed areas are such areas where the remunerative potential of the farmers is relatively more precarious. In this context, the present study was conducted on poultry farming in the rainfed areas of the Jammu region of South Asia, respectively. Four districts, i.e. Jammu, Kathua, Rajouri and Udhampur, were purposively selected for the study. Production function analysis along with marginal value productivity was employed to carry out the monetary impact of the different inputs to the total production. To rule out the errors on account of skewed population of the research sample, the artificial neural network (ANN) approach using sensitivity analysis was also being conducted. The study revealed the net returns to be highest in case of Kathua region on account of the reasons like easy availability and accessibility of input requirements, proximity to the Punjab state, etc. Further, production function analysis pointed out that the chick cost, feed cost and veterinary care were significant factors. Sensitivity analysis revealed the importance of veterinary care in Kathua and Rajouri district and quality of feed in Jammu and Udhampur districts, respectively. Looking at the findings of the study, the study concluded that due care must be given to the veterinary care and quality of chicks, to make the poultry enterprise more remunerative in the rainfed regions.

Maintaining stable yield in a climate change scenario requires the implementation of adaptive strategies for vine and vineyard management. Knowing the level of starch stored in perennial vine organs may help to reduce the risk of environmental constraints. Therefore, the development of decision support tools is important to assist winegrowers. The aim of this work was to test the possibility of estimating the starch content in grapevine woody tissues using digital imaging analysis. A colorimetric index (CI) based on red (R), green (G), and blue (B) mean pixel values was proposed and compared with an existing spectrophotometric index (SI). Shoots of three grapevine cultivars were collected during the 2020 dormant season and forced to produce new rooted cuttings. From these cuttings, a total of 73 internodes were sampled and their woody sections stained with Lugol’s iodine solution and CI and SI determined. In the three varieties, the CI well correlated with the SI (Spearman’s test ranged from − 0.84 to − 0.93, p < 0.0001). The resulting CI-based model developed can accurately (R² = 0.84) predict the SI confirming it is a reliable method for the digital determination of woody reserves in grapevine, supporting management decisions in viticulture.

To optimize use of land, farmers need to make decisions regarding grain varieties, rotation, different crop management systems, and whether to sow a single or double crop in a calendar year. In Córdoba (Argentina), the predominant crops are wheat, soybean and maize, sown as single crop (SC) or double crop (DC) sequences (wheat–soybean or wheat–maize). The objective of this work was to compare Phenofit algorithm and Envelope Crop Classification (ECC) method to identify the presence of SC or DC using MODIS-NDVI temporal series. Calibration and validation were carried out using field data acquired from 2015 to 2018. NDVI signatures of each plot were compared with SC and DC temporal NDVI profiles and the class membership was determined when at least 50% of values fell inside of one profile and the difference between classes was positive. The results showed that the ECC/Phenofit present overall accuracy between 96/90 and 98/92% and Kappa coefficients from 91/82 to 97/95%, respectively. On average, when the ECC was applied, the percentages of the study area detected as DC were between 18.3 and 28.7%, for the considered periods, while the area occupied with SC decreased from 64 to 49.5%. ECC and Phenofit are very good methods for detecting double crop.

Different agronomic practices can change the morphological and productive responses of forage cactus to the cultivation environment. Therefore, studying the effects of the management adopted is essential to improve crop performance. The objective was to evaluate how the phenology, the morphophysiological indicators, and the cutting moment of cactus forage are influenced by different agronomic practices. Four experiments were evaluated in the Brazilian semi-arid region between 2017 and 2020, using the following practices: cactus clones [Opuntia (OEM) and Nopalea (MIU and IPA)]; water availability (0%, 40%, 80%, 100% and 120% of crop evapotranspiration—ET_c); mulch levels (0, 5, 10, 15 and 18 Mg ha⁻¹); and intercropping [OEM/millet (cv. IPA Bulk-1-BF)]. Every 30 and 90 days, respectively, biometric and biomass data of the cultures were collected. Data were subjected to statistical analysis, and sigmoid regression models were generated. On average, the highest DM accumulation was observed in the OEM clone (0.0097 Mg ha⁻¹ °C day⁻¹) regardless of the system. The 80%ETc regimen favoured higher rates for Nopalea cacti. The exclusive cultivation of palm with cover favoured greater emission of cladodes (0.0583 units  °C day⁻¹) in relation to the intercropping. Regardless of the condition, the MIU cactus showed more phenophases. The adoption of practices reduced the cutting time of the cactus to 1225 °C day. Thus, management interventions must be adopted considering the cactus species present in the system, in order to improve the performance of the crop in the cultivation environment.

Determination of suitable land for cultivating different crops is an essential action for increasing agricultural productivity. The present research is carried out to zone suitable lands for cultivating rice, wheat, and barley by integrating intelligent methods and spatial data in the Silakhor plane of Iran. Machine learning methods, including artificial neural network and random forest in integration with geographic information system data, are used for zoning. The nine agro ecological parameters were used as input layers. The suitability level as an output layer is classified into four classes: very suitable (S1), suitable (S2), relatively unsuitable (S3), and not suitable (N). The 720 samples were considered to evaluate the models. The 70% of sample data were used for training, 15% for testing, and 15% for validation. The coefficient of variation, root mean square error, and area under the ROC (Receiver Operating Characteristics) curve were obtained to evaluate the performance of the models. Based on the results, both methods have sufficient validity to assess the land suitability of rice, wheat, and barley in the studied area. By using the ANN model, 56.1%, 67.1%, and 80.7% of the studied areas were suitable for cultivating rice, wheat, and barley, respectively. By using the RF model, 58.6%, 58.3%, and 62.6% of the studied areas were suitable for cultivating rice, wheat, and barley, respectively.

Deglet Nour, the most frequent in Algeria and Tunisia date palm cultivar is commonly referred to as “queen of all dates” due to its superior quality in terms of texture and taste. Date palms are dioecious plants and require artificial pollination to reach an economic yield. Despite the development in artificial pollination methods, hand pollination is still the most used technique in North African countries. However, the practices remain not well controlled particularly in terms of pollen quality. Therefore, enormous pollen quantity was used for pollination. In this study, the effect of four pollen densities was recorded, which represent 67, 87 and 97% lower than the control pollen quantity. The effect was observed on fruit set, retention and quality; seed germination; and plantlets vigor. The fruit size and weight (fresh and dry matter), moisture content, and yield were significantly reduced with the decreasing pollen density. The fruit set was not influenced by pollen density in maturation stages; kemari, khalal and rutab. However, fruit retention decreased significantly by pollen density. The seed germination was reduced significantly; while, there was no effect on seedling growth between all pollen treatments. It was found that the density of one (67% lower than control) and three strands (control) had the same effect on the investigated properties. The study provides significant insights regarding how different pollen densities will affect the fruit quality and yield of date palms.

Chemical fertilizer is not only very hazardous to crops but also to human health and is a critical environmental pollutant. In vineyards, a huge amount of chemical fertilizer was applied to get higher yield. However, the application of farmyard manure (FYM) in vineyards can be the best possible way to enhance the yield and sustain good soil health. FYM application is the most drudgery prone operation in grapes cultivation, mostly done manually. Therefore, an automated solution was developed and assessed for the purpose of dispensing FYM alongside the plant roots on both sides consistently. The FYM applicator consisted of a mixing chamber, a hydraulic motor with a flow control valve, a pair of augurs for dispensing FYM and a conveyor-type side dispensing unit. The FYM applicator is operated with a hydraulic motor. The amount of FYM required can be controlled with the flow control valve as well as with the tractor engine speed. The FYM applicator has a width of 2.3–2.6 m to match the vineyard row spacing of 2.5–3.0 m. Dunnett test indicated that the rate of FYM application significantly (p < 0.05) changes with tractor engine speed, while physical properties vary with the material used for fabrication of the hopper. The equipment can deliver FYM at the rate of 7.8 kg/min from each outlet at 1000 rpm of the tractor engine speed, as per recommended dose. The field capacity and field efficiency of the developed FYM applicator are found to be 0.2 ha/h and 71%, respectively. This technology saves the cost of operation, labour and time by 88, 98 and 80%, respectively, with the added advantage of uniform FYM application in the vineyards.

Soil P dynamics, as a function of soil buffering capacity, affecting P availability for plant use in different Iranian agricultural areas were investigated. The P buffering capacity and requirement of four different calcareous soils were investigated to determine: (1) P surface sorption using the isotherms models, and (2) the indices of buffering capacity and soil P requirement (SPR). Soil samples (0–30 cm) were collected from 26 different regions and following soil physicochemical analyses, 13 soils were selected for further investigation. The sorption isotherm models of Langmuir, Freundlich, Temkin and Van Huay were fitted to the selected soils to determine the P buffering indices including maximum buffering capacity (MBC, ranging from 7 to 127, average = 42 L kg⁻¹), P buffering capacity (PBC, ranging from 11 to 78, average = 31 L kg⁻¹), equilibrium buffering capacity (EBC, ranging from 17 to 80, average = 33 L kg⁻¹), and soil buffering capacity (SBC, ranging from 7 to 120 L kg⁻¹). The slopes of Temkin isotherm model (K₂) and Van Huay (B_VH) were used as the indices of buffering capacity (32 to 168, average = 87 L kg⁻¹) and 26.85–211.05 (average = 93 L kg⁻¹), respectively. The van Huay isotherm model was used to determine SPR (ranging from 42 to 225 L kg⁻¹, average = 120 L kg⁻¹). Soil indices were significantly and positively correlated with soil organic carbon and CaCO₃. It is possible to predict plant P requirement in the experimental regions, and the similar regions, worldwide, as functions of soil colloidal properties (P buffering capacity), using the tested models.