Planting Area Research Articles

Mapping annual 10-m soybean cropland with spatiotemporal sample migration

China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Unveiling antibiotic contamination in surface water: A study of the Huaihe River Basin's Huaibei Plain, a significant Chinese herbal medicine planting region

The abuse of antibiotics has caused the accumulation of antibiotic residues in environmental media, threatening the ecosystem and human health. Many studies on the distribution of aqueous antibiotics have been reported. However, the pollution status of antibiotics in the environment in Chinese herbal medicine planting areas is rarely comprehensively clarified, resulting in the lack of updated pollution data and conducive suggestions for ecological cultivation and sustainable development of Chinese herbal medicine. Thus, we comprehensively investigated the distribution, profiles, sources, and risks of the antibiotics in the surface water of an important tributary of the Huaihe River Basin, located in Bozhou City, a significant Chinese herbal medicine planting region. Solid-phase extraction coupled with an ultra-performance liquid chromatography-tandem mass spectrometer (SPE-UPLC-MS) was utilized to detect the antibiotics in the water. 27 kinds of antibiotics were identified with total concentrations ranging from 75.01 to 1737.99 ng·L−1, with doxycycline (DC) and doxycycline hydrochloride (DCH) possessed the highest concentration. And DC, DCH, oxilinic acid (OA), sulfamethoxazole (SMZ), clarithromycin (CLA), and roxithromycinum (ROX) were the main antibiotics detected in this basin. Correlation analysis and principal component analysis (PCA) indicated that animal husbandry was the primary source of antibiotics. Furthermore, the ecological risk assessment revealed that certain antibiotics could seriously threaten the survival of aquatic organisms, implying that local Chinese herbal medicines might be at similar growth risk. The drinking risk assessment showed that antibiotics in the water posed low risks for human, and children faced a greater drinking risk than adults. The study can help to facilitate the management of aqueous antibiotic pollution for the ecological cultivation and safe production of Chinese herbal medicine.

Neonicotinoid insecticides in well-developed agricultural cultivation areas: Seawater occurrence, spatial-seasonal variability and ecological risks

Neonicotinoids (NEOs) are widely used insecticides and have been detected in aquatic environments globally. However, little is known about NEOs contamination in the coastal environments under the terrestrial pressure of multiple planting types simultaneously. This study investigated the occurrence, spatial-seasonal variability, and ecological risks of NEOs along the coast of the Shandong Peninsula during the dry and wet seasons, where located many largest fruit, vegetable, and grain production bases in China. The concentrations of ∑NEOs in seawater were higher in wet seasons (surface: 195.46ng/L; bottom: 14.56ng/L) than in dry seasons (surface: 10.07ng/L; bottom: 8.45ng/L). During the wet seasons, NEOs peaked in the northern and eastern areas of the Shandong Peninsula, where the inland fruit planting area is located. While dry seasons had higher concentrations in Laizhou Bay, influenced by rivers from vegetable-growing areas. Grain crops, fruit, and cotton planting were major NEOs sources during wet seasons, while wheat and vegetables dominated in dry seasons. Moderate or above ecological risks appeared at 53.8% of the monitoring sites. Generally, NEOs caused high risks in the wet seasons mainly by Imidacloprid, and medium risk in the dry seasons by Clothianidin, which should be prevented and controlled in advance. Environmental ImplicationNeonicotinoid pesticides, an emerging contaminant, have been detected in many environmental media and exhibit toxicity to non-target organisms. The coastal waters of the Shandong Peninsula, the center of grain production, were selected as the research object. The temporal and seasonal variation coincides with the local planting types and the usage preference of NEOs. The potential sources of NEOs in wet seasons were generally rain crops, fruit, and cotton planting, whereas wheat and vegetables in dry seasons. Finally evaluated according to risk quotient showed that neonicotinoids were of moderate risk in 58.8% of study areas.

First Report of Fusarium falciforme Causing Root and Rhizome Rot of Epimedium sagittatum in China.

Epimedium sagittatum (Sieb.et Zucc.) Maxim., a perennial herb, is an important medicinal plant, rich in flavonoids, and widely used in the treatment of sexual dysfunction, rheumatic disease, and cancers (Tan et al. 2016). In July 2022, a disease of root and rhizome was found on E. sagittatum aged 1-8 years in a planting area (266 ha) of Zhumadian City (32°58'12" N, 114°37'48" E), Henan Province, China. The disease incidence per field (660 m2) was around 10-15% in six randomly surveyed fields planted with about 10,000 E. sagittatum plants each. Symptoms included leaf yellowing, root and rhizome browning, rotting and necrosis, and eventually the whole plant wilted and died. Fifteen plants with symptoms were sampled to isolate the pathogen. Symptomatic tissues were cut into small pieces of 5×5 mm, surface sterilized with 75% ethanol for 30 s, followed by three rinses with sterile double-distilled water (ddH2O). The pieces were then surface disinfected with 0.1% HgCl2 for 30 s, rinsed three times with sterile ddH2O, placed onto potato dextrose agar (PDA) plates and incubated at 28°C in the dark for 5 days. Twelve deferent Fusarium spp. colonies were purified by excising hyphal tip onto PDA for cultivation. Pathogenicity test of all strains was performed. Only isolate GY2 could result in root and rhizome rot of host plant. Colonies of GY2 on PDA had abundant white aerial mycelia with yellow halo. Macroconidia were hyaline, falciform, with a slightly curved apical cell and blunt basal cell, 29.7~45.0 (average 38.3) × 4.5~6.6 (average 5.3) µm (n =50), with 2-3 septa. Microconidia were oval, or reniform, hyaline, 8.4~26.5 (average 16.5) × 2.7~6.0 (average 4.5) µm (n =50), with 0-2 septa. Morphological characteristics of isolate GY2 were consistent with those of the Fusarium solani species complex (FSSC) (Chehri et al. 2015). For molecular identification, a region of the translation elongation factor 1-α (TEF) and RNA polymerase second largest subunit (RPB2) of GY2 were PCR-amplified and sequenced using the primers EF1-728F/986R (Carbone et al. 1999) and RPB2-5f2/7cr (O'Donnell et al. 2010), respectively. The TEF and RPB2 sequences (GenBank accession nos. OR978135.1 and OR978136.1) of GY2 were concatenated for a phylogenetic analysis using the Bayesian method (Zhang et al. 2020). The phylogenetic tree revealed that isolate GY2 clustered with F. falciforme with a credibility value of 99%. Morphological and molecular results support identification of isolate GY2 as F. falciforme. A pathogenicity test was performed on 4-year-old healthy plants grown in pots. Twenty healthy plants were inoculated by pouring a 200 mL conidial suspension (1×106 conidia/mL) around the rhizome. Control plants received 200 mL of sterile ddH2O. All treatments were maintained in a greenhouse at 25±1°C and 80% relative humidity. The assay was conducted three times. After 20 days, similar symptoms as those in the field were observed on the inoculated plants, whereas controls remained asymptomatic. Fusarium falciforme was reisolated from the symptomatic plants and showed the same morphological and molecular characteristics as isolate GY2, fulfilling Koch's postulates. Fusarium falciforme was reported to cause root rot of tobacco (Qiu et al. 2023) and industrial hemp (Paugh et al. 2022). However, this is the first report of F. falciforme causing root and rhizome rot of E. sagittatum. Our study will contribute to the development of strategies for the effective management of this disease on E. sagittatum.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Post-harvest monitoring and crop production assessment in Nertiti locality of Central Darfur State-Sudan

Crop productivity has declined all over the world due to weak investments in agricultural research, which negatively impact food production as well as food security situation. The purpose of this study is to explore the connection between post-harvest monitoring and food production security in Nertiti locality of Central Darfur state during 2012/2103 cropping rainy season. Descriptive statistical analysis by using Statistical Package for the Several Sciences (SPSS) was run. Three villages (Eraideeba, Salakoya and Rijlaldelaiba) were randomly selected. A total of 1600 (men & women) of beneficiary household head were targeted in the entire study area. Clustered random sampling technique applied. Crop harvest chick list and Focus Group Discussion were administered at the beneficiary and community level for planted areas, agricultural production and the effect on food and seed security. Due to, 20 households head randomly selected from each village. A total of 60 household head (40 men and 20 women) selected for chick list in addition to 15 household head for Focus Group discussion (10 men and 5 women) to represent the whole study area. Results of Statistical analysis related to crop yields indicated that the average yields of sorghum, millet and groundnut among improved seeds beneficiary managed farms exceed other traditional farms by 22%, 47% and 55%. Production at community level revealed that, Rijalldelaiba village gave highest millet production (1740 metric tons), Salakoyaproduced1409.4 metric tons of sorghum and Eriadeeba village gave 456 metric tons ofgroundnuts. Households also assessed that, their cultivated area increased by 33%, 50% same, while 17% said decreased. Group discussion and individual interview results related to food security and crop utilization showed that, food and seed security for 12 month is good, production covers consumption food for 8-12 month, the adopted coping strategies for the remaining 4 month were livestock sale, daily labor, migration, firewood collection, charcoal and bricks making. Regarding the crop utilization, the majority of respondents reported that, 50% left their by-products on the field, 27% used as animal fodder and 23% stored. A focus group discussion result addresses constraints of, problems of security, storage facilities, packing materials, marketing problems, extension services and inadequate improved seeds. Monitoring reached recommendation of, provision of improved seeds, training, marketing facilities and packing inputs.

Can Agricultural Insurance Policy Adjustments Promote a ‘Grain-Oriented’ Planting Structure?: Measurement Based on the Expansion of the High-Level Agricultural Insurance in China

Ensuring national food security is a perennial topic, and securing the grain planting area is an essential solution. Cost savings at scale from agricultural insurance policy adjustments could be a powerful incentive for grain production. In this study, 527 data sets from 31 provinces in China from 2006 to 2022 were used as the sample, and the author applied a multi-stage DID model to measure the effects of agricultural insurance policy adjustments on the grain planting area and planting structure, as well as the influence mechanisms behind them. The results can be summarized as follows: Firstly, agricultural insurance policy adjustments can make a significant contribution to increasing the grain planting area, with some positive impact on the ‘grain-oriented’ planting structure. Secondly, agricultural insurance policy adjustments can significantly increase the grain planting area by increasing the application of agricultural machinery, but this mechanism does not affect the ‘grain orientation’ planting structure. Thirdly, agricultural insurance policy adjustments can have a significant positive impact on the grain planting area and ‘grain—oriented’ planting structure in both high- and low-risk areas, with low-risk areas being more affected than high-risk areas.

Optimizing the co-benefits of biodiversity and carbon sinks in urban residential yards

Urban green infrastructure is recognized for its potential to combat biodiversity loss and enhance carbon sequestration in cities. While residential yards constitute a significant part of urban green infrastructure, their role in providing urban ecosystem services remains largely understated. There is a lack of systematic measures for effectively implementing urban vegetation to enhance ecosystem services. The aim of this study is to investigate how different vegetation types typically found in urban residential yards of apartment blocks can enhance carbon sequestration and biodiversity, and how these benefits can be supported through landscape design. The study encompasses an integrative literature review and qualitative analysis. Drawing from a review of previous research, this study identifies the drivers that indicate either carbon sink potential or biodiversity enhancement potential of urban vegetation types. The drivers are then cross-examined to identify the qualities of urban green that potentially strengthen carbon–biodiversity co-benefits. As the key findings we present versatile measures to enhance the potential co-benefits of carbon sinks and biodiversity within urban yards and summarize them in three main categories: plant diversity, provision of good growing conditions and maintenance. The study stresses that the several potential co-benefits of urban green can only be achieved through the selection and prioritization of solutions during the planning and design process. To exemplify this, we demonstrate how the findings from the literature review can be incorporated into the design and management of urban yards. We conclude that the main actions to be addressed in the future planning and design of urban residential yards are (i) establishing diverse planting areas with a mixture of woody and herbaceous plants to encourage species richness and complexity, (ii) optimizing the use of space and growth conditions, and, (iii) implementing maintenance practices that consider both carbon and biodiversity aspects. The study highlights that through enhancing carbon-biodiversity co-benefits urban yards can significantly contribute to major environmental challenges and provide vital ecosystem services within the built urban environments.

Developing and Expanding the Networks of Peanut Seed Producers of Department of Agriculture Thailand

Developing and expanding the networks of peanut seed producers of the Department of Agriculture. The objective is to develop and expand a network of seed producers ppeanut varieties are sold with quality appropriate to the local conditions farmers can produce peanut seeds and store the seeds for their own use be sold to farmers in need in nearby areas there is an increase in peanut seeds for use within the country and selecting farmers who produce peanuts in important production areas of Thailand, including Lopburi province Lampang province and Khon Kaen province by transfer the technology for producing peanut certified seed or extension seed is choosing a planting area, planting, disease and insects, roguing, seed conditioning, harvest and seed quality. Farmers produce peanut seeds under the guidance of the Department of Agriculture with monitoring and solve problems to develop farmers to be able to produce quality peanut seeds and developed into a network of peanut seed producers of the Department of Agriculture. In 2022, there were 15 network farmers able to produce of seed yield 8,406 kg. Seed quality, Moisture 5.3%, Germination 78.7% and Purity 97.3%. In 2023, there were 30 additional network farmers able to produce of seed yield 15,478 kg. Seed quality, Moisture 6.7, Germination 79.1% and Purity 96.3% which complies with peanut seed quality standards certified seed or extension seed.

First Report of Alternaria alternata Causing Leaf blight on Epimedium sagittatum (Sieb. et Zucc.) Maxim. in China.

Epimedium sagittatum (Sieb.et Zucc.) Maxim. is an important material of traditional Chinese medicine because of the rich content of flavonoids that are used to treat osteoporosis, liver cancer, and sexual dysfunction (Liu et al. 2013). A leaf blight was observed on E. sagittatum in Zhumadian City, China (32°58'12" N, 114°37'48" E, continental monsoon climate) in June 2021. Survey indicated that about 18% of the plants were infected in a 266-ha commercial planting area. The initial symptoms were white patches with tan borders, irregular in outline, with small black particles visible on the center of the lesions. In a week or so, patches extended throughout the leaf, and then leaves withered. Thirty leaves with symptoms collected from five different sites were cut into 5×5 mm pieces, and then surface-sterilized with 75% ethanol for 15 s followed by rinsing with double distilled water (ddH2O) three times. The pieces were then disinfested with 0.1% HgCl2 solution for 30 s, and rinsed with ddH2O, then placed onto potato-dextrose agar medium (PDA) and incubated in the dark for 3 d at 28°C. Eight fungal isolates were purified; of these, only the isolate HY2-1 infected the host plant and was selected for further morphological characterization. The colonies of HY2-1 were olive green with loose aerial hyphae on PDA. Conidiophores were single or branched, producing brown conidia in short chains. Conidia were obclavate, obpyriform, or ellipsoidal, 15.9-47.3 µm × 7.6-16.6 µm (n=50) and pale brown or dark brown with a short cylindrical beak at the tip that contained 1-5 transverse septa and 0-4 longitudinal septa. Morphological characteristics of the isolate were identical with those of Alternaria species (Huang et al. 2022). For molecular identification, the internal transcribed spacers (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Weir et al. 2012), major allergen Alt a 1(Alt a 1) and translation elongation factor 1-α gene (TEF) (Lawrence et al. 2013) were amplified and sequenced using the primers ITS4/5, GDF/GDR, Alt-F/R, and EF1-728F/986R, respectively. The results of the sequencing were uploaded to GenBank as ITS (OR418487), GAPDH (OR419792), Alt a 1 (OR419794), and TEF (OR419796), respectively. Phylogenetic analyses were performed by concatenating all the sequenced loci using the Bayesian method in Phylosuite (Zhang et al.2020). The phylogenetic tree indicated that the isolate belongs to the A. alternata clade with a bootstrap value of 75%. The pathogen was identified as A. alternata based on the morphological and molecular results. To satisfy Koch's postulates, a conidial suspension (106 conidia/mL) of the HY2-1 was prepared with ddH2O to infect the healthy plants. Ninety healthy leaves on 30 plants in pots were punctured using a sterilized needle, and then inoculated by spraying the conidial suspension on the wounded leaves in a greenhouse at 25°C and 80% relative humidity. The control plants were sprayed with ddH2O. The plants showed similar symptoms to the original infected plant 15 d after inoculation. The controls showed no symptoms. A pure culture of A. alternata was isolated and identified again as previously described. Leaf blight caused by A. alternata has been reported on Taro (Liu et al. 2020), Toona ciliata (Wang et al. 2023), etc. To our knowledge, this is the first report of E. sagittatum leaf blight caused by A. alternata in China. The results will help to develop effective control strategies for leaf blight on E. sagittatum.

YIELD STABILITY OF PURPLE CORN HYBRIDS (Zea mays L.) IN SOUTHERN SONORA, MEXICO

Corn (Zea mays L.) production in Mexico is in deficit despite generating 27 million Mg. In southern Sonora, the planted area has grown significantly over the last four years, from 544 to 680 thousand Mg produced in 2018 and 2021, respectively. Ninety-seven percent of the production corresponds to improved white-colored maize from transnational companies; however, there are no commercial plantings of colored maize. The objective of this research was to estimate grain yield and its agronomic components in improved purple corn hybrids in southern Sonora, Mexico, over three years using the additive main effects and multiplicative interaction method (AMMI). A randomized, complete-block experimental design with three replications was used. The plots were two 4 m long furrows with a stocking density of 100 000 plants ha-1. The variables recorded were: grain yield (RG), thousand-kernel weight (PMG), ear length (LM), grains per row (GH), rows per ear (HM), and hectoliter weight (PH). The results showed statistical differences among years, hybrids, and their interaction (AxH) in all variables. The AMMI model was highly effective, allowing the identification of hybrids 1, 2, 7, and 10 with greater stability in the year 2021 associated with RG, PH, and LM, obtaining outstanding averages in all variables and being more appropriate for the climatic conditions of southern Sonora. On the contrary, the most sensitive hybrid with the highest interaction was 4, associated with GH and HM; hybrids 3, 5, 6, 8, 9, 11, and 12 were stable with negative values in 2019 and 2020.

TREE PLANTING PRIORITIZATION IN NATIONAL CAPITAL REGION, PHILIPPINES USING REMOTE SENSING, ANALYTIC HIERARCHY PROCESS AND GIS

Abstract. The need for trees in cities is rapidly increasing because of global warming, rapid population growth, and urbanization. To maximize the benefits of trees, areas of greatest need should be identified and prioritized. In this study, a geospatial framework for tree planting prioritization is developed for NCR using remotely sensed datasets, AHP, and GIS. A Planting Priority Index (PPI) for NCR is derived based on five criteria with corresponding weights calculated based on ratings from 18 experts – air quality (25.2%), tree cover (23.8%), land cover (17.8%), population (17.4%), and land surface temperature (15.8%). The AHP resulted in a consistency ratio (CR) value of 1.7% and a consensus value of 53.2%. PPI values were calculated and statistically significant prioritization clusters and outliers were identified using Anselin Local Moran’s I statistics. The resulting PPI and cluster maps showed that the high-priority areas for tree planting clustered near the region’s center and northwest portion covering the cities of Malabon, South Caloocan, Navotas, Quezon City, Marikina, San Juan, and Mandaluyong, while the low priority areas were found mostly along the region’s outskirts at cities of Pasay, Las Piñas, Muntinlupa, Taguig, Valenzuela, and North Caloocan. The generated maps showing PPI values across the region may aid local government agencies and environmental organizations in evaluating and recalibrating their local greening programs. The workflow presented in this study can also be adopted in other regions with localized variables and site-specific goals relevant to tree planting and greening programs.

Sugarcane Water Productivity for Bioethanol, Sugar and Biomass under Deficit Irrigation

Knowledge of how certain crops respond to water stress is one of the prerequisites for choosing the best variety and best management practices to maximize crop water productivity (WPc). The selection of a more efficient protocol for managing irrigation depths throughout the cultivation cycle and in the maturation process at the end of the growth period for each sugarcane variety can maximize bioethanol productivity and WPc for bioethanol, sugar and biomass, in addition to the total energy captured by the sugarcane canopy in the form of dry biomass. This study aimed to evaluate the effect of four irrigation depths and four water deficit intensities on the maturation phase for eight sugarcane varieties under drip irrigation, analyzing the responses related to WPc for bioethanol, sugar and biomass. These experiments were conducted at the University of São Paulo. The plots were positioned in three randomized blocks, and the treatments were distributed in a factorial scheme (4 × 8 × 4). The treatments involved eight commercial varieties of sugarcane and included four water replacement levels and four water deficits of increasing intensity in the final phase of the crop season. It was found that for each variety of sugarcane, there was an optimal combination of irrigation management strategies throughout the cycle and during the maturation process. The RB966928 variety resulted in the best industrial bioethanol yield (68.7 L·Mg−1), WPc for bioethanol (0.97 L·m−3) and WPc for sugar (1.71 kg·m−3). The energy of the aerial parts partitioned as sugar had a direct positive correlation with the availability of water in the soil for all varieties. The RB931011 variety showed the greatest potential for converting water into shoots with an energy of 1.58 GJ·ha−1·mm−1, while the NCo376 variety had the lowest potential at 1.32 GJ·ha−1·mm−1. The productivity of first-generation bioethanol had the highest values per unit of planted area for the greatest water volumes applied and transpired by each variety; this justifies keeping soil moisture at field capacity until harvesting time only for WR100 water replacement level with a maximum ethanol potential of 13.27 m3·ha−1.

Modern Floating Greenhouses: Planting Gray Oyster Mushrooms with Advanced Management Technology Including Mobile Phone Algorithms and Arduino Remote Control

A floating greenhouse for growing oyster mushrooms can be operated remotely via a mobile phone. This innovative system can enhance mushroom production and quality while saving time. By using the Android OS operating system on a mobile phone (Internet Mobile Device with Android OS, MGT Model: T10), users can adjust the humidity and temperature within the greenhouse. This approach is particularly beneficial for older adults. Create a smart floating greenhouse that can be controlled remotely to cultivate oyster mushrooms. It would help to enhance the quality of the mushrooms, reduce the time required for cultivation, and increase the yield per planting area. We carefully examined the specifications and proceeded to create a greenhouse that could float. In addition, we have developed a unit that could control temperature and humidity, a solar cell unit, and a rack for growing mushrooms. Our greenhouses were operated remotely. To determine the best conditions for growing plants in a floating greenhouse, we conducted a test to measure temperature and humidity. We then compared our findings to those of a traditional greenhouse test and determined the optimal parameters for floating greenhouse growth. These parameters include growth time, temperature, humidity, and weight. A mushroom nursery that can be controlled remotely and floats on water consists of four main components: a structure to regulate temperature and humidity, solar cells, and mushroom racks. Research shows that mushrooms grown under this automated control system grow better than those grown through traditional methods. The harvest period is shorter, and the yield is higher than the typical yield of 1.81–1.22. When considering the construction and use of remote-controlled floating mushroom nurseries, the daily weight of mushrooms accounted for 20.22%. The company’s investment return rates were found to be 3.47 years, or 580.21 h per year, which is higher than the yield of traditional methods. This mobile phone remote control system, created by Arduino, is tailor-made for cutting-edge floating greenhouses that grow grey oyster mushrooms. It can be operated with ease via mobile devices and is especially user-friendly for elderly individuals. This system enables farmers to produce a high volume of quality breeds. Furthermore, those with fish ponds can utilize the system to increase their profits.

Response of agronomic and physiological traits of flue-cured tobacco (Nicotiana tabacum L.) to plant stem cell soil amendments

Black shank disease is an important factor limiting the yield of flue-cured tobacco. Soil amendments have the potential to improve soil health and increase crop yield and quality. However, the application effects of soil amendments have not been elucidated in flue-cured tobacco. Here, we conducted field experiments in the major tobacco planting area, Exploring the effects of plant stem cell soil amendments at different levels on the occurrence and physiological growth of tobacco black shank disease, The purpose is to evaluate the effectiveness of plant stem cell soil amendments and recommend feasible applied rates. Plant stem cell soil amendments are applied at four levels: T0 (0 kg ha−1), T1 (1500 kg ha−1), T2 (2250 kg ha−1), and T3 (3000 kg ha−1). Nutrient absorption, agronomic traits, dry matter quality, chemical composition, and economic benefits of flue-cured tobacco black shank disease were explored. The results showed that the application of plant stem cell soil amendments effectively reduced the incidence rate of tobacco black shank disease, and significantly improved the agronomic traits (leaf number, plant height, leaf length, and width) and dry matter quality of flue-cured tobacco. At the same time, the nutrient content (N, P, and K), the proportion of high-quality tobacco leaves, and the content of coordinated chemical components (total nitrogen, total plant alkali, total sugar, reducing sugar, potassium and chlorine) of flue-cured tobacco plants were increased. In addition, the application rate of stem cell soil amendment at 3000 kg ha−1 showed the best effect on reducing the occurrence of black shank disease, which resulted in improved tobacco yields and economic benefits to the greatest extent of flue-cured tobacco. The study indicates that plant stem cell soil amendments can significantly enhance the yield and quality of flue-cured tobacco and offers an effective method for sustainable crop production in the tobacco industry.

Identity, pathogenicity and genetic diversity of Lasiodiplodia associated with stem-end rot of avocado in China.

Stem-end rot (SER) causes brown necrotic lesions in the pulp near the base of the fruit pedicel and is one of the most devastating postharvest diseases of avocados in all avocado growing regions of the world. China's avocado industry is growing very rapidly, and the planting area is expanding, but little is known about the pathogens and genetic diversity of avocado SER. To determine the causal agents of SER, avocado fruits were sampled from the main avocado-producing areas in China during 2020 and 2021. Fungal isolates were obtained from SER symptomatic avocado fruits and identified by morphology combined with phylogenetic analysis of internal transcribed spacer (ITS), translation elongation factor 1-α (EF1-α) and β-tubulin (TUB2) gene sequences. All 101 isolates belonged to Lasiodiplodia spp., and four Lasiodiplodia species were identified, namely L. pseudotheobromae (59.41%), L. theobromae (24.75%), L. mahajangana (7.92%), L. euphorbiaceicola (1.98%), and six others are classified as Lasiodiplodia sp. (5.94%). There were only slight morphological differences in colonies and conidia of these four species of Lasiodiplodia. The pathogenicity tests showed symptoms of SER, and the 92.08% of the isolates exhibited a high level of virulence on avocado (disease index > 70), related to the disease severity on avocado fruit. All tested isolates grew well under conditions from 23 to 33℃. There was a significant difference in mycelial growth between the four species of Lasiodiplodia after treatment with high temperature or low temperature. L. pseudotheobromae growth was the fastest at 13 to 18℃, but was the lowest at 38℃ (P < 0.05). Red pigment could be produced by all tested isolates after culturing for 7 days at 38℃. The mycelial growth rate was the fastest on PDA medium, and the slowest on OMA medium but promoted spore formation (P < 0.05). In addition, was determined the genetic diversity of Lasiodiplodia pathogenic species associated with SER collected from avocado, mango, guava and soursop fruits was determined. A total of 74 isolates were clustered into 4 main ISSR groups by unweighted pair-group method with arithmetic mean (UPGMA) analysis, and the classification of this group was related to the host. Extensive diversity was detected in the Lasiodiplodia populations. The diverse geographical origins and host species significantly influenced the population differentiation, and most of the genetic variation occurred within populations (P < 0.001). This is the first study to identify the major pathogens of avocado SER in China and to survey their occurrence, pathogenicity and include a comparative analysis of genetic diversity with Lasiodiplodia spp. causing SER on other fruit hosts. Collectively, the Lasiodiplodia species complex affecting avocado showed high pathogenicity and diversity, while L. pseudotheobromae was the most frequently isolated species in China. The results of this study provide insights into the aspects of epidemic of SER disease caused by Lasiodiplodia species, which will help in developing strategies for the management and control of stem end-rot in avocado.

Productive performance of cassava (Manihot esculenta Crantz) in the Maranhense Mesorregions of the Amazon Biome

Cassava (Manihot esculenta Crantz) is a cultivar that is widely distributed in the mesoregions of the state of Maranhão in Brazil, and is highly prized for its value as a food crop, economic sphere and social and cultural contribution. In addition, the species is adaptable to different biomes, culminating in its agricultural expansion throughout the country. In this context, the aim of this study is to highlight the socio-economic importance of cassava in the mesoregions of Maranhão that cover the Amazon Biome, since both agriculture and livestock farming have an impact on the use and preservation of this area. To this end, a statistical analysis was carried out using the IBGE Automatic Retrieval System (SIDRA) for the 2015-2022 inter-annual period for the mesoregions of North, West and Central Maranhão, including the Biome under study, in terms of productivity. As a result, it was possible to see a sharp drop in cassava production in the Northeast, from 816.259 ha-1 (2010) to 390.472 ha-1 (2022) of planted area, a reduction of 52,17%. In addition, the pattern of decline followed for harvested area, quantity produced and average yield. In summary, despite the respective importance of cassava, the crop faces major challenges as it competes with other economically valuable crops for arable land, as well as competition from strong cattle farming. The fact is that just producing cassava is not enough to sustain it. It is necessary to master the practices and techniques that optimize the cassava agricultural production system.

Cross-dataset discriminant subspace learning algorithm for apple leaf diseases identification

Apple has a large planting area and high yield in China, but it is easily affected by diseases. Artificial intelligence technology has achieved good results in apple leaf disease identification. However, the training and testing data in complex environments often come from different collection scenarios. There are significant differences in the training and testing datasets, which leads to a significant decline in the identification performance. To improve the generalization ability of apple leaf disease identification, we develop a cross-dataset discriminant subspace learning (CDDSL) algorithm by utilizing the idea of transfer learning, low-rank sparse representation, and maximum margin neighborhood preserving embedding (NPE) to cross-dataset scenarios. Firstly, based on the transfer learning and low-rank sparse representation, each target sample can be represented by a linear combination of source domain samples. The sparse constraint on the noise matrix weakens the influence of noise in the sample data. Then, CDDSL progressively projects the subspace features into the semantic space and establishes a discriminant classifier. Meanwhile, CDDSL utilizes the maximal margin NPE to improve the intraclass compactness and interclass separability of projection features, which can enhance the image identification performance. The experiments conducted on real-world apple leaf datasets verify the effectiveness of the CDDSL algorithm in cross-dataset scenario.

Mapping tobacco planting areas in smallholder farmlands using Phenological-Spatial-Temporal LSTM from time-series Sentinel-1 SAR images

Phenological information on crop growth aids in identifying crop types from remote sensing images, but its incorporation into classification models is insufficiently exploited, especially in deep learning frameworks. This study presents a new model, Phenological-Temporal-Spatial LSTM (PST-LSTM), for mapping tobacco planting areas in smallholder farmlands using time-series Sentinel-1 Synthetic Aperture Radar (SAR) images. The PST-LSTM model is built on a multi-modal learning framework that fuses phenological information with deep spatial–temporal features. We applied the model to extract tobacco planting areas in Ninghua, Pucheng, and Shanghang Counties, in Fujian Province, and Luoping County in Yunnan Province, China. We compared PST-LSTM with existing methods based on phenological rules and Dynamic Time Warping (DTW) methods, and analyzed its strength in feature fusion. Results showed that our model outperformed these methods, achieving an overall accuracy (OA) of 93.16% compared to 86.69% and 85.93% for the phenological rules and DTW methods, respectively, in the Ninghua area. PST-LSTM effectively integrated time-series data with phenological information derived from different strategies at the feature level and performed better than existing feature fusion methods (based upon fuzzy sets) at the decision level. It also demonstrated a better spatial transferability than other methods when applied to different study areas, achieving an OA of 90.95%, 91.41%, and 80.75% for the Pucheng, Shanghang, and Luoping areas, respectively, using training samples from Ninghua. We conclude that PST-LSTM can effectively extract tobacco planting areas in smallholder farming from time-series SAR images and has the potential for mapping other crop types.

Методический подход к оценке экономической эффективности рисоводства

Domestic rice growing is characterized by a high level of capital intensity and technological advancement of the industry. The high deterioration of irrigation and drainage structures, the imperfection of the organizational and economic mechanism, and low investment attractiveness have led to a decrease in the rice planting area, gross yield and economic efficiency. In this regard, there is a need to develop methodological approaches to assessing the economic efficiency of the industry. The purpose of the article is to present a methodological approach to assessing the economic efficiency of rice farming, based on the calculation of an integral efficiency indicator. Theoretical methods of semantic analysis and synthesis, abstract-logical method and comparison method are used in the study. The article presents a methodology for assessing the economic efficiency of rice farming based on the compatibility of indicators that reflect all aspects of the economic activities of rice-growing organizations. An assessment of the current state of the rice growing industry in the Krasnodar region is given. Rice-growing organizations in the region are ranked according to a comprehensive integral index. The proposed approach will enable to obtain reliable information about all parameters of the production process in the industry, adapted to the rice growing characteristics and natural and climatic conditions of the region, as well as to justify priority directions for increasing the economic efficiency of rice growing in the region.