Planting Area Research Articles

Identification and Characterization of Colletotrichum Species Associated with Anthracnose Disease of Plum.

Plum (Prunus salicina Lindl.) is commercially cultivated worldwide for the high levels of nutrients in the fruit. In recent years, anthracnose has been severe in some plum planting areas in China, resulting in a large number of necrotic leaves, blight and pre-mature leaf fall. In this study, anthracnose samples of plum leaves were collected from Hezhou, Guilin and Lipu, Guangxi Province, and Meishan city, Abe Tibetan and Qiang autonomous prefecture of Sichuan Province. Characteristics of mycelia on PDA, morphology of appressoria and conidia, and analysis of sequences of several marker regions (internal transcribed spacer [ITS] region, glyceraldehyde-3-phosphate dehydrogenase [GAPDH], chitin synthase [CHS-1], histone H3 [HIS3], actin [ACT], β-tubulin [TUB2], and the intergenic region between apn2 and MAT1-2-1 [ApMat]). The resulting 101 Colletotrichum isolates obtained were identified as eight species: C. fructicola (50.5%), C. siamense (24.8%), C. karsti (8.9%), C. plurivorum (7.9%), C. aeschynomenes (3.9%), C. gloeosporioides (2%), C. celtidis (1%) and C. phyllanthi (1%). Representatives of all eight Colletotrichum species were found to cause disease on wounded leaves of plum seedlings in pathogenicity assays. As far as we are aware, this is the first report of anthracnose of plum caused by C. celtidis and C. phyllanthi in China.

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Genome-wide identification of the walnut MYC gene family and functional characterization of Xinjiang wild walnut under low-temperature stress

Introduction: MYC transcription factors are the basic regulators of the jasmonic acid signaling pathway and play important roles in plant growth and development and the response to adverse stress. In recent years, severe winter freezing and late spring frost in the main planting area of walnut in Xinjiang have affected the growth and development of walnut, which has become a prominent problem restricting walnut production. Xinjiang wild walnut is the only remaining wild species of walnuts in China, which contains a lot of genes with excellent traits, and is important for the cultivation and breeding.Methods: In this paper, the physicochemical properties and bioinformatics of MYC transcription factor members in walnut were analyzed, and the nine MYC were screened from the transcriptome data under low temperature stress. At last, we study the subcellular localizations and the expression patterns of the nine MYC members in Xinjiang wild walnut.Results: The results revealed that 30 MYC members were identified from published walnut whole-genome data, and their evolutionary relationships with Arabidopsis and poplar were divided into six groups according to clustering analysis, among which JrMYC22 and JrMYC23 had high homology with PtrMYC2b, which is induced by jasmonic acid in response to low-temperature stress. Walnut MYC members are unevenly distributed on 12 chromosomes. The prediction of promoter cis-acting elements of walnut MYC transcription factor family members revealed that cis-acting elements related to jasmonic acid and lowtemperature stress were the ones with the greatest number of members, with 12. In addition, all nine MYC family members in Xinjiang wild walnut plants responding to low-temperature stress exhibited strong fluorescence responses in the nucleus. The expression levels of these members in response to low-temperature stress revealed that JrMYC28, JrMYC31, JrMYC33, JrMYC34, and JrMYC35 were highly expressed, and it was hypothesized that JrMYC28, JrMYC31, JrMYC33, JrMYC34, and JrMYC35 might play a key role in the response to lowtemperature stress.Discussion: The results of this study provide a theoretical basis for further research on the functional mechanisms of the MYC transcription factor family members in walnut.

Comparison of Extraction accuracy of Sugarcane from different resolution satellite images using Deep lab V3+ Mode

Abstract. Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting.

Comprehensive analysis of stress-activated protein kinase genes (OsSAPKs) in rice flowering time.

The rice SnRK2 members SAPK4, SAPK5, SAPK7 and SAPK10 are positive regulators involved in the regulation of rice flowering, while other single mutants exhibited no effect on rice flowering. The rice SnRK2 family, comprising 10 members known as SAPK (SnRK2-Associated Protein Kinase), is pivotal in the abscisic acid (ABA) pathway and crucial for various biological processes, such as drought resistance and salt tolerance. Additionally, these members have been implicated in the regulation of rice heading date, a key trait influencing planting area and yield. In this study, we utilized gene editing technology to create mutants in the Songjing 2 (SJ2) background, enabling a comprehensive analyze the role of each SAPK member in rice flowering. We found that SAPK1, SAPK2, and SAPK3 may not directly participate in the regulatory network of rice heading date, while SAPK4, SAPK5, and SAPK7 play positive roles in rice flowering regulation. Notably, polygene deletion resulted in an additive effect on delaying flowering. Our findings corroborate the previous studies indicating the positive regulatory role of SAPK10 in rice flowering, as evidenced by delayed flowering observed in sapk9/10 double mutants. Moving forward, our future research will focus on analyzing the molecular mechanisms underlying SAPKs involvement in rice flowering regulation, aiming to enhance our understanding of the rice heading date relationship network and lay a theoretical foundation for breeding efforts to alter rice ripening dates.

Remote Sensing-Based Monitoring of Cotton Growth and Its Response to Meteorological Factors

Cotton is an important economic crop and strategic resource. Monitoring its growth and analysing its response to meteorological factors are crucial for field management and yield estimation. This study selects the primary cotton-producing regions in northern Xinjiang as the study area. Firstly, using the Google Earth Engine cloud platform, the Cotton Mapping Index (CMI) was utilised to extract cotton planting areas from 2019 to 2023. Secondly, Sentinel-2A data were used to calculate the NDVI of cotton during the growing season and analyse its variation characteristics. Finally, correlation, lag, and partial correlation analyses were conducted between cotton NDVI and meteorological factors, including effective accumulated temperature, wind speed, precipitation, and solar shortwave radiation, to explore the response relationship. The results indicate the following: (1) The optimal classification threshold of CMI in the study area was determined to be 0.74, which was applied to extract cotton planting areas over the years. The overall classification accuracy achieved was 84.85%. The R2 value for the cotton area extracted by CMI compared to the cotton planting area in the statistical yearbook data is 0.98, with an average relative error of 16.84%. CMI’s classification use effectively distinguishes cotton from other major crops, such as wheat and corn, in the study area. Compared with different classification methods, CMI is more convenient and efficient for extracting cotton planting areas, contributing significantly to yield estimation and management. (2) We found that from 2019 to 2023, some fields were planted with cotton yearly. In order to prevent land degradation, a crop rotation system should be implemented, in which cotton rotates with other crops to reduce the rate of soil nutrient loss and achieve sustainable agricultural development. (3) NDVI can effectively monitor the spatiotemporal changes and regional variations in cotton growth. Sentinel-2 multi-spectral imagery possesses high spatial and temporal resolution, enabling effective monitoring of cotton growth, provision of cotton growth data for field managers, and application in cotton production management. Additionally, cotton yield estimation can be achieved by comparing the overall growth of cotton across different years. (4) Cotton NDVI exhibits a strong correlation with effective accumulated temperature and solar radiation, with the majority passing the significance test, suggesting a significant promotion effect on cotton growth by accumulated temperature and solar radiation. In cotton cultivation management, attention should be directed toward monitoring changes in accumulated temperature and solar radiation. Moreover, NDVI changes in response to solar radiation exhibit a certain lag. The correlation between NDVI and precipitation is low, likely attributed to local cotton cultivation primarily relying on drip irrigation. Cotton NDVI is negatively correlated with wind speed. Cotton planting should consider weather changes and take corresponding preventive management measures. The research results have significant reference value for monitoring cotton growth, disaster prevention, and sustainable agricultural development.

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

Calibration and Verification of Discrete Element Parameters of Surface Soil in Camellia Oleifera Forest

To analyze the interaction between the surface soil and the soil-contacting component (65 Mn) in the camellia oleifera forest planting area in Changsha City, Hunan, China, in this study, we conducted discrete element calibration using physical and simulation tests. The chosen contact model was Hertz–Mindlin with JKR cohesion, with the soil repose angle as the response variable. The repose angle of the soil was determined to be 36.03° based on the physical tests. The significant influencing factors of the repose angle determined based on the Plackett–Burman test were the soil–soil recovery coefficient, soil–soil rolling friction coefficient, soil-65 Mn static friction coefficient, and surface energy of soil for the JKR model. A regression model for the repose angle was developed using the Box–Behnken response surface optimization method to identify the best parameter combination. The optimal parameter combination for the JKR model was determined as follows: surface energy of soil: 0.400, soil–soil rolling friction coefficient: 0.040, soil-65 Mn static friction coefficient: 0.404, and soil–soil recovery coefficient: 0.522. The calibrated discrete element parameters were validated through experiments on the repose angle and steel rod insertion. The results indicated that the relative errors obtained from the two verification methods were 2.44% and 1.71%, respectively. This research offers fundamental insights for understanding the interaction between soil and soil-contacting components and optimizing their design.

Mapping the Land Use Changes in Cultivation Areas of Maize and Soybean from 2006 to 2017 in the North West and Free State Provinces, South Africa

Land use practices face significant pressure due to increased demand and conflicting needs. Several factors contribute to this trend, such as the ever-increasing human population, the increased demand for food production, and the expansion of industrial and agricultural areas. This paper, focused on the cultivation patterns and investigating changes in land use of maize and soybean over time (i.e., both genetically modified and non-genetically modified) in two South African provinces. The objective was to determine whether there was a net increase or decrease in land cover age for these two crops between 2006 and 2017 in the selected study areas. Hence, the study utilized ArcGIS (10.8.1) software to quantify and map the land used for the cultivation of maize and soybean from 2006 to 2017 in Free State and North West provinces. The results show both provinces to have minimal expansion or change in cultivation areas for both maize and soybean between 2006 and 2017. We concluded that both maize and soybean cultivation areas in these provinces, did not expand beyond the current agricultural areas (space), and did not encroach onto new land areas. As a result, both maize and soybean do not currently pose a threat to the surrounding landscape (i.e., natural vegetation) and are not in direct competition with other neighboring land use practices. We recommend that data on the annual planting or cultivation area be consistently gathered, analyzed, and mapped to monitor any alterations that could influence the current findings. This will also assist with any land use planning and management practices.

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.

Variations in Cold Resistance and Contents of Bioactive Compounds among Dendrobium officinale Kimura et Migo Strains

Dendrobium officinale is a valuable traditional Chinese herbal plant that is both medicinal and edible. However, the yield of wild Dendrobium officinale is limited. Adverse stress affects the growth, development, and yield of plants, among which low temperature is the primary limiting factor for introducing Dendrobium officinale to high-latitude areas and expanding the planting area. Therefore, this study aims to explore the variations in growth ability, cold resistance, and contents of bioactive compounds among different Dendrobium officinale strains. Four strains of Dendrobium officinale were selected as experimental materials and were subjected to low-temperature stress (4 °C). The agronomic traits, physiological indices, as well as the expressions of cold resistance-related genes (HSP70, DcPP2C5, DoCDPK1, and DoCDPK6) in the roots and leaves of Dendrobium officinale, were determined. The contents of bioactive compounds, including polysaccharides, flavonoids, and phenols were also measured. Compared with the other strains, Xianju had the highest seed germination and transplantation-related survival rates. Under low-temperature stress, Xianju exhibited the strongest cold resistance ability, as revealed by the changes in water contents, chlorophyll levels, electrical conductivities, enzyme activities, and expressions of the cold resistance-related genes. Additionally, the polysaccharide content of Xianju increased the most, while the stem flavonoid and leaf phenol contents were elevated in all four strains under cold treatment. Therefore, selecting excellent performing strains is expected to expand the planting area, improve the yield, and increase the economic benefits of Dendrobium officinale in high latitude areas with lower temperatures.

GACDNet:Mapping winter wheat by generative adversarial cross-domain networks with transformer integration for zero-sample extraction

Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information of the same feature varies from image to image. Machine learning methods are very popular in extracting winter wheat planting area, there are differences between different images, namely the distribution differences between source domain data and target domain data, the results obtained by these methods directly applied to other areas are not satisfactory. To achieve cross regional extraction of winter wheat area, we propose generative adversarial cross-domain networks for image classification under the same image type. The cross-domain network proposed comprises a generative network and a feature extractor. The generative network creates diverse and acceptable samples from the initial input data, constrained by the contrast loss. The feature extractor converts the initial input data and the generated data into a high-level representation. Two time series datasets were constructed and a series of experiments were conducted on these datasets, obtaining high-precision and optimal classification results. Sample training was performed using 70%, 50%, 30% and 10% of the data on both datasets. The study results demonstrated the high performance and cross-domain capability of our network. Furthermore, the winter wheat cultivation area of the entire Zhoukou city was extracted, yielding results with an accuracy of 94.23%. Extensive experiments and applications on the dataset demonstrate that our method is feasible and reliable.

National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series

Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km2 (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the “Sickle Bend” region of China (the “Sickle Bend” policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.

Difference in Cd accumulation among varieties with different growth duration corresponding to typical agro-climate condition in rice ratooning system

IntroductionThe ratoon rice planting area is gradually expanding, but there has been relatively little research on ratoon rice grains contaminated with Cd.MethodsIn this study, five ratoon rice varieties were selected and divided into three groups according to early-maturity (growth duration: 100–110 days), mid-maturity (growth duration: 110–120 days) and late-maturity (growth duration: 120–130 days) varieties. Field experiments were done to study the differences in Cd accumulation among ratoon rice varieties with different growth duration.ResultsThe results showed that the Cd accumulation and concentration of grains spikelet at each growth stage in the main crop were in the order of late-maturity > mid-maturity > early-maturity varieties. However, the trends in Cd concentration and accumulation in grains spikelet during the ratoon crop were the opposite. Analysis found that as the growth duration of the variety extended, the accumulated temperature and daily average temperature in the main crop increased, which significantly increased the translocation factors of Cd from root, stem, and leaf to grains spikelet, and increased the daily average Cd accumulation rate in grains spikelet. The daily average temperature in the ratoon crop increased as the growth duration shortened. The early-maturity variety had higher Cd accumulation in stubble, which promoted the translocation of Cd from the root, stem, and leaf of the plant to the grains spikelet.DiscussionTherefore, appropriately shortening the growth duration of the main crop and extending the growth duration of the ratoon crop are important ways to reduce Cd accumulation in ratoon rice in areas with mild Cd pollution.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Explanatory Model for the Estimation of the Minimum Monoculture Planted Area for Coffee that would allow reaching a Living Income

This study's objective was the construction of an explicative model to determine the minimum area necessary to be planted in coffee, to afford a living income, on monoculture coffee farms. In the sustainable coffee production scenario, the annual net income of families is evaluated in function of the achievement of a decent standard of living for all of its members. The metric which makes this possible is called living income. Unfortunately, among small coffee farmers, there is a large gap between real net income and living income, which is a multidimensional phenomenon, affected by exogenous, difficult-to-control factors, such as area cultivated in coffee and its sale price, in addition to variables derived from productive models employed, such as productivity, costs, and farm food production. Due to it being a multifactorial phenomenon, data mining algorithms were applied (decision tree and random forest), in order to classify those farms that would close the gap between net and living income. Model iterations were performed on a dataset that gathered historical production cost, income, and cultivated area information from farms located in four Colombian departments. It was concluded that the variables which explain the gap in a monoculture coffee system are as follows: Area planted in coffee, productivity, and sale price.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

First Report of Pseudopestalotiopsis ixorae Causing Leaf Spot of Photinia × fraseri in China.

Photinia × fraseri is a well-known ornamental shrub in southern China. In December 2021, we observed leaf spots that were circular to irregular, gray with dark red margins and violet brown with brownish violet edges on the leaves of Photinia × fraseri shrubs in the scenic area of Shenlongtan (28°46'10″N, 115°42'93″E), Jiangxi Province, China. Almost 15% of the leaves in the 1300 m2 Photinia × fraseri planting area were symptomatic. Thirty symptomatic leaves were randomly collected from different plants, and sectioned into 5-mm2 pieces, which were surface-sterilized using 1% NaOCl for 30 s. After rinsing thrice in sterile distilled water and drying, the pieces were transferred onto potato dextrose agar (PDA) and incubated at 28 ℃ for 5-7 days. A total of sixteen morphologically similar isolates were obtained. After incubation on PDA for 20 days, the fungi had irregular edges, were white to pale brown, and had spare aerial mycelium on the surface with irregularly distributed black, gregarious conidiomata. Conidia were fusoid, subcylindrical, straight to slightly curved, 4-septated, slightly constricted at the septa, and 23 to 36 × 6 to 10 μm (mean: 27.6 × 7.7 μm). The morphological characteristics were consistent with the features of Pseudopestalotiopsis species (Maharachchikumbura et al. 2014). The genomic DNA of two representative isolates (JFRL032 and JFRL033) was extracted for further identification. The internal transcribed spacer (ITS) region, translation elongation factor 1-ɑ (tef1-ɑ) and β-tubulin (tub2) genes were amplified and sequenced using primers ITS5/ITS4, EF1-526F/EF1-1567R, and Bt2A/Bt2B, respectively (Maharachchikumbura et al. 2012). The sequences of the two representative isolates were 100% identical to each other. These nucleotide sequences were deposited in GenBank with accession numbers, ON342794 and ON342795 (ITS); ON375851 and ON375852 (tef1-ɑ); ON375853 and ON375854 (tub2). BLASTn searches of the obtained sequences revealed 99%-100% to ITS (MG816316, 478/478 nucleotides), tef1-ɑ (MG816336, 924/926 nucleotides), tub2 (MG816326, 441/442 nucleotides) sequences of the ex-type strain of Pseudopestalotiopsis ixorae (NTUCC17-001.1). Phylogenetic analysis was conducted using the concatenation of multiple sequences (ITS, tef1-ɑ and tub2) with the Maximum likelihood statistics in PhyloSuite v1.2.2 (Zhang et al.2020). The phylogenetic tree showed the two isolates clustered with P. ixorae in a clade with 100% bootstrap support. The isolates were identified as P. ixorae based on morphological and molecular data. To confirm pathogenicity, eight healthy leaves of 3-year-old Photinia × fraseri were surface sterilized, scratched with a pair of sterilized tweezers, and ten µl of conidial suspension (106 conidia/ml) was sprayed on the injured leaves and the control was sprayed with sterile distilled water. Then, All plants were potted in a climate chamber at 25℃ and 85% relative humidity. After 3 days, leaf spot symptoms similar to those described above were observed on inoculated leaves, while the non-inoculated leaves remained symptomless. The pathogen was reisolated from the inoculated leaves to fulfill Koch's postulates and confirmed as P. ixorae by morphological and molecular analysis. It has been reported that P. ixorae can infect the Ixora plant (Tsai et al., 2018). To the best of our knowledge, this is the first report of P. ixorae causing leaf spot on Photinia × fraseri in China. The study provides valuable information for identifying and controlling the leaf spot on Photinia × fraseri.

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.