Hasil untuk "Agricultural industries"

K. Murase, Y. Hirano, Tai-ping Sun et al.

M. Venkatesh, G. Raghavan

Simin Zhang, Limin Duan, Yongzhi Bao et al.

Dryland ecosystems are highly sensitive to global change, with their carbon–water cycling strongly regulated by soil moisture (SM). The critical soil moisture content at which the ecosystem shifts from energy limitation to water limitation during drought can be regarded as a soil moisture threshold that characterizes this regulation. Accurately quantifying critical soil moisture thresholds (θt) remains a key scientific challenge in elucidating the mechanisms of carbon–water interactions. This study focuses on the Horqin Sandy Land, one of the four major sandy regions in northern China, using long-term eddy covariance and environmental data collected from dune and meadow ecosystems during the growing seasons (May–September) from 2013 to 2024. Three methods—evaporative fraction (EF), covariance (Cov), and correlation difference (corr)—were used to identify to θt, marking the transition from energy-limited to water-limited states during drought. Furthermore, interpretable machine learning (XGBoost–SHAP) and dominance analysis (DA) were employed to elucidate the driving mechanisms of θt. Results showed that meadow ecosystems had stronger carbon uptake capacity, water regulatory potential, and environmental stability than dune ecosystems. The three methods yielded highly consistent θt for both dune (θtEF: 0.0676, θtCov: 0.0564, θtcorr: 0.0544 m³/m³) and meadow (θtEF: 0.4335, θtCov: 0.4178, θtcorr: 0.3934 m³/m³) ecosystems, with standard deviations of 0.005 and 0.023 m³ /m³ , respectively. In dunes, θt was primarily driven by rainfall (Rain) and evapotranspiration (ET), with gross primary productivity (GPP) and ET jointly contributing over 50 % to its formation. In meadows, θt was regulated by Rain and canopy conductance (gc), with GPP and gc contributing 28.98 % and 19.57 %, respectively. As shown for the two ecosystems examined, the dune and meadow ecosystems exhibit pronounced differences in the critical soil moisture thresholds at which the system shifts between water and energy limitation. Given that drylands encompass a wide range of ecosystems—including grasslands, forests, and croplands. This highlights the necessity of establishing coordinated, multi-ecosystem observational networks across drylands to compare responses systematically and clarify vegetation–water interactions. This is essential for effective water resource management and ecosystem restoration in arid regions.

Haihong GUO, Yuqin ZHU

Abstract Improving agricultural carbon productivity is crucial for advancing economic and social green and low-carbon development, with the digital development initiative exemplified by the digital village construction pilot policy being instrumental in enhancing agricultural “quality and efficiency.” This study treats the pilot policy for digital rural development as a quasi-natural experiment of external shock policy, employing the difference-in-differences (DID) method to dynamically assess its impact mechanisms and pathways on agricultural carbon productivity using panel data from 620 counties (cities and districts) in major grain-producing regions from 2018 to 2022. The findings reveal that the implementation of the digital rural development pilot policy effectively promotes a significant increase in local agricultural carbon productivity, with adjacent regions experiencing similar effects. Among these, inland regions exhibit weaker policy effects compared to coastal regions, and the policy relationships in urban–rural integrated development zones and east-central-west collaborative development zones also follow this pattern. Similarly, the policy effects of large-scale operations are superior to those of small-scale operations. The policy further highlights the two key mechanisms—industrial agglomeration and service integration—underlying its impact on China’s agricultural carbon productivity. Additionally, the increase in agricultural entrepreneurship activity exhibits a trend of increasing marginal benefits in relation to policy implementation.

Y. Basiron

M. Jacob Schrader, Dattatray G. Bhalekar, Ramesh K. Sahni et al.

Unmanned Aerial Sprayers (UASs) are being sought after as a possible alternative to knapsack sprayers in topographically challenging vineyards. However, prior work has identified that UASs have issues with delivering adequate spray mix to the bottom canopy zones. This study was thus conducted to understand and potentially improve spray delivery to the grapevine bottom canopy zones through two flight patterns (cross-row and row-aligned) and three commercial UASs platform configurations (AGRAS T20, AGRAS T30, and AGRAS T30 ‘orchard configuration’, DJI Technology Co.). Three flights were conducted for each treatment at an application rate of 109 L ha-1 (11.47 GPA) over independent replicate test blocks. All tests were conducted in a vertical shoot position (VSP) trained vineyard (cv. Chardonnay). Water sensitive papers and mylar cards were used to quantify coverage (%) and deposition (ng cm-2), respectively. Overall, cross-row flight applications (coverage: 6.70 ± 1.38 % [mean ± standard error], deposition: 104.54 ± 17.71 ng cm-2) failed to provide significantly different (p > 0.05) spray delivery as compared to the row-aligned operations (coverage: 10.10 ± 2.14 %, deposition: 83.81 ± 11.17 ng cm-2). Similarly, coverage and deposition data collected for the three UAS geometries failed to present significant differences (p > 0.05). Regardless of configuration or flight pattern, the bottom canopy zone received significantly lower deposition than the top canopy zone across treatments. Further engineering towards optimization of UAS geometry is needed for efficient chemical application in modern VSP trained vineyards.

Aftab Haider Khan, Nuaman Ejaz, Songhao Shang et al.

Efficient water and land management is crucial for sustainable agriculture, particularly in regions facing growing water scarcity and urbanization pressures. This study aims to evaluate the cropping patterns and irrigation water use in eight Canal Command Areas (CCAs) of Bari Doab, an agriculture-dominated region in Punjab, Pakistan. Based on Sentinel-2 satellite imagery and field survey results, cropping patterns across all CCAs during the Rabi and Kharif seasons from 2018 to 2023 were identified using the Random Forest (RF) algorithm, which achieved high crop classification accuracy with the overall accuracy reaching 89.9 % for the Rabi and 90.1 % for the Kharif season crops. Producer and user accuracies ranging from 90.3 %–90.8 % and 88.6 % underscore the reliability of the classification approach. Crop water requirements are estimated according to crop classification results in each CCA using the Penman-Monteith method, revealing higher crop evapotranspiration (ETc) for Kharif crops than Rabi crops, which is driven by seasonal climatic differences. Spatial analysis indicated consistent cropland decline in CCA3 and CCA7 due to urbanization, with statistical cropland areas decreasing by 127 km² and 96 km² between 2018 and 2023, respectively. Groundwater abstraction increased steadily across all CCAs, with the highest increasing rates observed in southern regions cultivating water-intensive crops. Groundwater storage anomalies (GWSA) were estimated from observed groundwater level data and the terrestrial water storage (TWS) component of Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) by subtracting non-groundwater components acquired from the Global Land Data Assimilation System GLDAS v2.2. GWSA anomalies revealed long-term depletion trends in all CCAs, with the steepest declines in CCA7 and CCA8 despite moderate abstraction for crops. This indicates that population-driven groundwater stress plays a significant role in these areas. The findings provide valuable insights for policymakers and stakeholders to balance agricultural demands with water resource sustainability in arid and semi-arid regions.

Gokhan Camoglu, Kursad Demirel, Fatih Kahriman et al.

Today, the use of sensors and imaging techniques, which are used to obtain information about plants and soil in smart irrigation systems, is rapidly becoming widespread. This study aimed to investigate the usability of leaf turgor pressure and thermal images from plant-based monitoring techniques to detect water stress and the irrigation time of pepper (Capsicum annuum L. cv. ''California Wonder'') and to determine their relationship with physiological traits in Canakkale/Türkiye in 2017 and 2018. The four irrigation treatments (100%, 75%, 50%, and 25%) were applied in the experiment. Leaf turgor pressure (Pp), thermal images and physiological measurements were carried out during the growing season. Soil moisture and Pp were monitored in real time by remote. Thermal and physiological measurements were made before each irrigation. As a result of the study, the average evapotranspiration (ETc) was 697 mm, and the yield value was 83.7 t ha−1 under non-stress conditions. Depending on the decrease in ETc, yield values also decreased significantly. Leaf water potential and stomatal conductivity values were statistically different in all irrigation treatments. The change in the activity of catalase (CAT) due to water stress was greater than that of superoxide dismutase (SOD). In this case, it can be said that other physiological traits are more successful than SOD in distinguishing water stress. According to the regression models, significant relationships were determined between both the indices calculated from the thermal images and Pp, yield, and physiological traits. The predictive ability of Pp values has been strengthened with the addition of meteorological properties to the model in general. The highest correlation (R2 =0.63) was between Pp + meteorological properties and CAT. All the regression models between physiological traits and indices calculated from thermal images were statistically significant. The highest R2 values were obtained in August. In this month, the highest correlations were between Crop Water Stress Index (CWSIp) and leaf water potential / stomatal conductivity (R2 =0.91), IGp and stomatal conductivity (R2 =0.80). The predictive power of CWSIp was higher than Stomatal Conductivity Index (IGp). The experiment illustrated that Pp and temperature data, which are plant-based monitoring methods, have the potential to detect water stress in peppers.

Xuguang Sun, Baoyuan Zhang, Menglei Dai et al.

The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat.

Matteo Ippolito, Dario De Caro, Marcella Cannarozzo et al.

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference evapotranspiration (ETo) represents the atmospheric water demand in each crop type, development stage, and management practices. The Penman-Monteith equation in the version suggested by the Food and Agriculture Organization (FAO56-PM), is one of the most used methods to estimate ETo. In several regions of the world, meteorological observations are not always available. The most recent reanalysis database ERA5-Land, released in 2019, can be useful to overcome this limit. The database provides, with a spatial grid of 0.1° latitude and 0.1° longitude, several hourly climate data such as air temperature, dew point temperature, solar radiation, and wind speed components all at 2.0 m above the soil surface, except wind speed components at 10 m, useful to apply the FAO56-PM equation. The objective of this research is to assess the quality of ERA5-Land climate variables data to estimate daily ETo in Sicily, Italy. The effect of the weather station’s elevation associated with the statistical indicators was also evaluated to verify how the morphology affects the measurements. Finally, the sensitivity analysis of the FAO56-PM equation was carried out to identify which climate variables have the most influence on the ETo estimation. For the period 2006–2015, the comparison between air temperature, global solar radiation, wind speed, and relative air humidity, measured from 39 ground weather stations in Sicily, and ERA5-Land was carried out and then, through FAO56-PM equation daily ETo values were estimated using both databases. The statistical indicators Root Mean Square Error (RMSE) and Mean Bias Error (MBE) confirm the possibility of considering the ERA5-Land a suitable solution to estimate ETo. The sensitivity analysis showed that good ETo estimation depends mainly on the accuracy of the relative air humidity and air temperature data.

Yingfen Jiang, Da‐Wen Sun, Hongbin Pu et al.

Kenthorai Raman Jegannathan, P. Nielsen

Abstract Enzymatic processes have been implemented in a broad range of industries in recent decades because they are specific, fast in action and often save raw materials, energy, chemicals and/or water compared to conventional processes. A number of comparative environmental assessment studies have been conducted in the past 15 years to investigate whether these properties of enzymatic processes lead to environmental improvements and assess whether they could play a role in moving toward cleaner industrial production. The purpose of this review is to summarize and discuss the findings of these studies and to recommend further developments regarding environmental assessment and implementation of the technology. Life Cycle Assessment (LCA) has been widely used as an assessment tool, while use of the ‘carbon footprint’ concept and Environmental Impact Assessment (EIA) is limited to a few studies. Many studies have addressed global warming as an indicator and several studies have furthermore addressed other impact categories (acidification, eutrophication, photochemical ozone formation, energy and land use). The results show that implementing enzymatic processes in place of conventional processes generally results in a reduced contribution to global warming and also a reduced contribution to acidification, eutrophication, photochemical ozone formation and energy use to the extent that this has been investigated. Agricultural land has been addressed in few studies and land use savings appear to occur in industries where enzymatic processes save agricultural raw materials, whereas it becomes a trade-off in processes where only fossil fuels and/or inorganic chemicals are saved. Agricultural land use appears to be justified by other considerable environmental improvements in the latter cases, and the results of this review support the hypothesis that enzyme technology is a promising means of moving toward cleaner industrial production. LCA gives a more complete picture of the environmental properties of the processes considered than EIA and carbon footprint studies, and it is recommended that researchers move toward LCA in future studies. Tradition, lack of knowledge and bureaucracy are barriers to implementation of enzymatic processes in industry. Education and streamlining of public approval processes etc. are means of overcoming the barriers and accelerating the harvesting of the environmental benefits.

William Stiles, D. Styles, Stephen P. Chapman et al.

Managing organic waste streams is a major challenge for the agricultural industry. Anaerobic digestion (AD) of organicwastes is a preferred option in the waste management hierarchy, as this processcangenerate renewableenergy, reduce emissions from wastestorage, andproduce fertiliser material.However, Nitrate Vulnerable Zone legislation and seasonal restrictions can limit the use of digestate on agricultural land. In this paper we demonstrate the potential of cultivating microalgae on digestate as a feedstock, either directlyafter dilution, or indirectlyfromeffluent remaining after biofertiliser extraction. Resultant microalgal biomass can then be used to produce livestock feed, biofuel or for higher value bio-products. The approach could mitigate for possible regional excesses, and substitute conventional high-impactproducts with bio-resources, enhancing sustainability withinacircular economy. Recycling nutrients from digestate with algal technology is at an early stage. We present and discuss challenges and opportunities associated with developing this new technology.

S. Berger, Yassine El Chazli, A. F. Babu et al.

Agricultural industry uses pesticides to optimize food production for the growing human population. A major issue for crops is fungal phytopathogens, which are treated mainly with azole fungicides. Azoles are also the main medical treatment in the management of Aspergillus diseases caused by ubiquitous fungi, such as Aspergillus fumigatus. However, epidemiological research demonstrated an increasing prevalence of azole-resistant strains in A. fumigatus. The main resistance mechanism is a combination of alterations in the gene cyp51A (TR34/L98H). Surprisingly, this mutation is not only found in patients receiving long-term azole therapy for chronic aspergillosis but also in azole naïve patients. This suggests an environmental route of resistance through the exposure of azole fungicides in agriculture. In this review, we report data from several studies that strongly suggest that agricultural azoles are responsible for medical treatment failure in azole-naïve patients in clinical settings.

Balaji Veeramani, J. Raymond, Pritam Chanda

Maize is a leading crop in the modern agricultural industry that accounts for more than 40% grain production worldwide. THe double haploid technique that uses fewer breeding generations for generating a maize line has accelerated the pace of development of superior commercial seed varieties and has been transforming the agricultural industry. In this technique the chromosomes of the haploid seeds are doubled and taken forward in the process while the diploids marked for elimination. Traditionally, selective visual expression of a molecular marker within the embryo region of a maize seed has been used to manually discriminate diploids from haploids. Large scale production of inbred maize lines within the agricultural industry would benefit from the development of computer vision methods for this discriminatory task. However the variability in the phenotypic expression of the molecular marker system and the heterogeneity arising out of the maize genotypes and image acquisition have been an enduring challenge towards such efforts. In this work, we propose a novel application of a deep convolutional network (DeepSort) for the sorting of haploid seeds in these realistic settings. Our proposed approach outperforms existing state-of-the-art machine learning classifiers that uses features based on color, texture and morphology. We demonstrate the network derives features that can discriminate the embryo regions using the activations of the neurons in the convolutional layers. Our experiments with different architectures show that the performance decreases with the decrease in the depth of the layers. Our proposed method DeepSort based on the convolutional network is robust to the variation in the phenotypic expression, shape of the corn seeds, and the embryo pose with respect to the camera. In the era of modern digital agriculture, deep learning and convolutional networks will continue to play an important role in advancing research and product development within the agricultural industry.

Samsu Udayana Nurdin, Siti Restia Salita, Celly Oktaviani et al.

High levels of carbohydrate consumption, especially starch, are considered to be an important risk factor for diabetes mellitus (DM). Conjugation of starch with phenolic compounds those have antidiabetic activity such as catechin is suggested increase health benefit of the starch.  Objective of this research was to find out optimal catechin concentration that was able to be conjugated into corn starch to produce conjugated starch with high antidiabetic and antioxidant properties. The synthesis of starch-catechin conjugates used free-radical grafting (FRG) by adding different concentration of catechin, namely 0%, 0,5%; 1%; 1,5%; and 2%. The result of the research indicates that increasing of catechin concentration grafted into starch increase the phenolic content of the starch. Conjugation of catechin into starch posed higher antidiabetic and antioxidant activities. Starch conjugated with 2.0% catechin had the best antidiabetic and antioxidant activities, therefore, it had potentiality to develop as functional starch for diabetes patients.

Biniyam Mulugeta Abuhayi, Yohannes Agegnehu Bezabih

Chickpea is one of the most important legumes in the world, however, it is prone to various diseases that can significantly reduce its yield and quality. Hence, the accurate classification of these diseases are crucial for effective disease management. In this study, we propose a combined approach for chickpea disease classification using GLCM-Color Histogram features with Bilateral filtering and non-local means filtering. Our research comprises three phases: image preprocessing, feature extraction, and classification. To enhance the model's robustness and reduce noise, we applied Bilateral filtering, non-local means filtering, and data augmentation techniques. We utilized a combination of gray-level co-occurrence matrix (GLCM) and Color Histogram for feature extraction, which can capture the texture and color features important for image classification tasks. The extracted features were then classified using Multi-Layer Perceptrons (MLPs), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The experimental results indicate that the combined features extracted using GLCM and Color Histogram with the SVM classifier outperformed individual feature extractors and classifiers, achieving a testing accuracy of 95.49 %. Our study demonstrates that proper image preprocessing, data augmentation, and feature extraction provide an efficient classification method for identifying and classifying chickpea disease.

Cristina-Emanuela Enascuta, Elena-Emilia Sirbu, Radu Claudiu Fierascu et al.

Annually, considerable amounts of polluted water are produced due to industrialization based on processing in various industries. The purpose of this study refers to a process of obtaining a photocatalytic system with a structure of metal oxides in the field of ultrasound, used in the advanced treatment of wastewater resulting from the pharmaceutical and agricultural industries. These photocatalytic systems allow wastewater to be treated in a relatively short time, being quickly recovered and reused repeatedly. To quantify the efficiency of the photocatalyst in treating wastewater, experiments were carried out in the presence of sunlight, in the absence and presence of the photocatalyst, with a methylene blue solution.

M. Tripoli, J. Schmidhuber

R. C. Kasana, C. B. Pandey