Rice Cultivation Area Research Articles

MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images.

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China's Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.

The delineation of management zones using soil quality indices for the cultivation of irrigated rice (Oryza sativa L.) in Huila, Colombia

Conventional intensive farming systems can result in degraded soil. It is therefore important to monitor this effect periodically by delineating management zones (MZ) based on soil quality indices (SQI) in order to maintain and improve the soil characteristics in a precision farming environment and obtain homogeneous rice yields. The aim of this study was to determine and spatialise SQI and delineate MZ for cultivating flooded rice in an area of Fluvisols in Huila, Colombia. Forty-one georeferenced soil samples were collected from the 0 to 20 cm layer, and the physical, chemical and biological attributes of the soil were analysed to calculate the Integrated Quality Index (IQI) and the Nemoro Quality Index (NQI) using linear scoring functions. Geostatistical tools were then used to fit semivariogram models of the SQI, and interpolated using ordinary kriging to map the MZ using the QGIS software. The IQI and NQI showed a moderate spatial correlation, which allowed three distinct MZ to be identified and delineated. Attributes, such as bulk density (Bd), total porosity (TP), soil respiration (SR), available water (AW) and soil organic matter (SOM) were significant and can be used as a guide by farmers for restoring the quality of the soil in rice production. The method proved to be effective, and provided an information base to be used in the local management of areas of rice cultivation in the study region.

Herbicide management for horseweed (Conyza spp.) control in post-emergence under flooded rice

This study aimed to assess the efficacy of commonly used post-emergence herbicides under flooded rice fields, for Conyza spp. control. The greenhouse experiment employed a factorial setup with factor A denoting the presence or absence of a water layer post-application, and factor B representing different herbicides, to C. sumatrensis. In field trials, were applied when the rice plantation was on V3 stage. Evaluations were control (%), roots and shoot dry weight of Conyza spp., as well as injury (%), number of grains per panicle, and grain yield (ha−1) of rice. The applications of imazapyr + imazapic, florpyrauxifen-benzyl, imazethapyr, penoxsulam, quinclorac, bentazon, triclopyr-butyl, saflufenacil and ethoxysulfuron followed by water layer entry led to Conyza spp. control surpassing 80% at 28 DAA. The herbicides florpyrauxifen-benzyl, quinclorac, triclopyr-butyl, and saflufenacil, without flooding after application, demonstrated Sumatran fleabane control equal to or greater than 80% at 28 DAA. Effective water layer management is crucial for Conyza spp. control in zero-grade rice cultivation areas.

Rice CRYPTOCHROME-INTERACTING BASIC HELIX-LOOP-HELIX 1-LIKE interacts with OsCRY2 and promotes flowering by upregulatingEarly heading date 1.

Flowering time is a crucial adaptive response to seasonal variation in plants and is regulated by environmental cues such as photoperiod and temperature. In this study, we demonstrated the regulatory function of rice CRYPTOCHROME-INTERACTING BASIC HELIX-LOOP-HELIX 1-LIKE (OsCIBL1) in flowering time. Overexpression of OsCIB1L promoted flowering, whereas the oscib1l knockout mutation did not alter flowering time independent of photoperiodic conditions. Cryptochromes (CRYs) are blue light photoreceptors that enable plants to sense photoperiodic changes. OsCIBL1 interacted with OsCRY2, a member of the rice CRY family (OsCRY1a, OsCRY1b, and OsCRY2), and bound to theEarly heading date 1(Ehd1) promoter, activating the rice-specific Ehd1-Heading date 3a/RICE FLOWERING LOCUS T 1pathway for flowering induction. Dual-luciferase reporter assays showed that the OsCIBL1-OsCRY2 complex required blue light to induce Ehd1 transcription. Natural alleles resulting from nonsynonymous single nucleotide polymorphisms in OsCIB1L and OsCRY2 may contribute to the adaptive expansion of rice cultivation areas. These results expand our understanding of the molecular mechanisms controlling rice flowering and highlight the importance of blue light-responsive genes in the geographic distribution of rice.

Assessing climate resilience in rice production: measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and machine learning

Satellite remote sensing (RS) and machine learning can be combined to develop methods for measuring the impacts of climate change on biomass and agricultural systems. From 2015 to 2023, we applied this approach in a critical earth observation-based evaluation of the Irrigation and Water Resources Management component of the Millennium Challenge Corporation’s Senegal Compact. This project, funded by the United States Agency for International Development (USAID), was implemented in the Senegal River Valley from 2010 to 2015. Utilising these techniques, we successfully mapped rice cultivation areas, deciphered cropping practices, and analysed irrigation systems responses to different climatic conditions. A marked increase in cultivated rice area was found particularly in regions targeted by the project intervention. This is despite prolonged drought conditions which underscores a significant climate adaptation benefit from these irrigation works. We observed a notable dip in rice cultivation area in 2020, possibly due to the COVID-19 pandemic, followed by a recovery to pre-pandemic levels in 2023, likely aided by previously funded USAID’s socio-economic resilience programmes in the region. Economic analysis of increased rice yields in the region translates to approximately US$ 61.2 million in market value since 2015, highlighting the economic returns from the project investment. Both the RS data and ground audits identify issues regarding post-project deterioration of irrigation infrastructure, emphasising the need for long-term maintenance of irrigation infrastructure to support climate adaptation benefits arising from irrigation. With a focus on crop irrigation, our findings stress the critical role of climate adaptation interventions for maintaining agricultural productivity in the face of adverse climate shocks. It further highlights the necessity of continuous investment and maintenance for ensuring climate resilient agrifood systems.

Adaptation test of rainfed rice fields highland in Jambi Indonesia

Abstract Increasing national rice productivity can be achieved by optimizing existing resources, including rainfed rice fields in highlands areas. Rainfed rice fields in Indonesia are quite diverse in agroecosystem conditions, ranging from lowlands to highlands. Indonesia has the potential for highlands that can be planted with rice to support national rice production. The high variety of biophysical, edaphic, and biotic conditions of the main agroecological areas of rice cultivation makes it difficult to obtain varieties that can grow well in all types of agroecosystems. The purpose of this study is to determine the potential yield and adaptability of rice hope strains to anaerobic germination conditions in rainfed land and highland rainfed rice strains. Ten rice strains and two reference varieties from The Indonesian Centre for Rice Research Sukamandi in West Java were tested in 2021, with four repetitions. The area of each plot of 4 x 5 m was carried out on rainfed rice fields in Kerinci regency with an altitude of 788.5 meters above sea level and a Sungai Penuh regency of 795 meters above sea level. Observations were made of vegetative, generative parameters of plants, as well as temperature and humidity. Results showed that the strains with the highest production and medium harvest age had good observational agronomic parameters, namely in strains B15617E-4-MR-3, BP29337-2-CRB-0-SKI-0-7-PWK-1-SKI-3-3, and BP19980-JK-2-IND-2-SKI-0-PWK-1-SKI-5-1.

Securing China’s rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models

Ensuring the security of China’s rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil properties and agricultural statistics from 2000 to 2017. The research evaluates six artificial intelligence (AI) models including machine learning (ML), deep learning (DL) models and their hybridization to predict rice production across China, particularly focusing on the main rice cultivation areas. These models were random forest (RF), extreme gradient boosting (XGB), conventional neural network (CNN) and long short-term memory (LSTM), and the hybridization of RF with XGB and CNN with LSTM based on eleven combinations (scenarios) of input variables. The main results identify that hybrid models have performed better than single models. As well, the best scenario was recorded in scenarios 8 (soil variables and sown area) and 11 (all variables) based on the RF-XGB by decreasing the root mean square error (RMSE) by 38% and 31% respectively. Further, in both scenarios, RF-XGB generated a high correlation coefficient (R2) of 0.97 in comparison with other developed models. Moreover, the soil properties contribute as the predominant factors influencing rice production, exerting an 87% and 53% impact in east and southeast China, respectively. Additionally, it observes a yearly increase of 0.16 °C and 0.19 °C in maximum and minimum temperatures (Tmax and Tmin), coupled with a 20 mm/year decrease in precipitation decline a 2.23% reduction in rice production as average during the study period in southeast China region. This research provides valuable insights into the dynamic interplay of environmental factors affecting China’s rice production, informing strategic measures to enhance food security in the face of evolving climatic conditions.

Assessing paddy methane emissions through the identification of rice and winter crop areas using Sentinel-2 imagery in Korea

The global efforts on reducing methane (CH4) emissions was emphasized in COP 28 and the potential for improved estimation became feasible through bottom-up data acquisition with advanced remote sensing technology. The objectives of this study were to extract summer rice and winter crop cultivation areas based on satellite images and to incorporate into estimating CH4 emissions in South Korea for the year 2020. Satellite images of Sentinel-1 and Sentinel-2 were acquired from European Space Agency. Rice paddy was classified with backscattering coefficient from Sentinel-1 images, while the normalized difference vegetation index from Sentinel-2 images was used to identify winter cropping field. The equation of IPCC guidelines was used to estimate CH4 emissions by incorporating the areas of rice paddy and winter crop extracted with the respective satellite image. National farming statistics were used to determine the scaling factors for paddy organic matter and water management practices. The estimated areas for rice paddy and winter crop cultivation were 712,237 ha and 117,840 ha, respectively. The rice paddy areas were primarily concentrated in the western regions of the Korean peninsula, whereas winter crop cultivation was predominantly found in southern part of the country. The total amount of CH4 emissions was 6272 Gg CO2 eq./yr when considering rice straw and winter cropping practices into estimation (modified Tier 2 method). This represents a 7% increase compared to the method that considered solely the rice straw incorporation (current Tier 2 method). The CH4 emissions per unit area were also 8.82 tons CO2 eq./ha/yr with the modified Tier 2 method, indicating a 10% greater compared to the current Tier 2 method. Substantial CH4 emissions were primarily concentrated in western regions where extensive rice paddy cultivation occurs, while greater CH4 emissions per unit area were predominantly found in southern regions with substantial winter crop cultivation. The study findings hold importance for improving the accuracy of CH4 emissions estimation by employing bottom-up approach that utilizes satellite imagery to assess rice paddy and winter cropping areas. Further study would be needed to incorporate field-based data on rice crop management practices, such as rice straw and water management, to further refine CH4 emission estimation method.

Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data

In recent decades, Vietnam has gradually become a critical global rice producer. During that production process, residual straw becomes an environmental pollutant due to open burning, raising greenhouse gas emissions. This study combines the optical images of the Sentinel-2 satellite and the radar images of the Sentinel-1 satellite to estimate the dry biomass of rice and to determine gas emissions due to rice straw burning over the fields in Quoc Oai district, Hanoi city for urban environmental management purposes. Sentinel-2 images have been classified into the land covers, thereby identifying the areas of ​​rice cultivation and the areas of ​​burned straw. Meanwhile, the Sentinel-1 radar image has been used to calculate the dry biomass of rice due to its ability to penetrate clouds, an obstacle to optical images in tropical regions. Furthermore, a field trip during harvesting season allows us to measure aboveground dry biomass. Then, the analysis shows a high correlation between the backscatter V.V. and V.H. of the radar image and the in-situ dry biomass (R=0.923 and R2=0.852), with a relatively low average error (RMSE = 6.58 kg/100 m2). By linear regression method, the study found the total rice dry biomass of 28728.5 tons, which was obtained after the Summer rice crop 2020 for the whole Quoc Oai district, of which 2037.91 tons of rice straw have been burned, releasing a large amount of greenhouse gas emission with 2398.6 tons of CO2, 189.5 tons of CO, 18.8673 tons of PM10 dust, 17.2087 tons of PM2.5 dust and some other gases. The identical procedure has also been applied to the western region of Hanoi city center to estimate the amount of gas emissions. This study has proven the effectiveness of an approach and contributed to supporting urban managers in proposing appropriate policies to monitor and protect the environment.

Enhancing Rice Productivity and Ensuring Food Security in Indonesia Through The Adoption of Innovative Technologies in Tidal Swamp Rice Farming

The expansion of rice cultivation areas in tidal swamp lands is crucial for Indonesia to increase rice production in order to meet the rising food demand in line with population growth. The purpose of this study is to comprehensively explore and assess innovative technologies that can be developed for rice cultivation in tidal swamp lands. The research was conducted using ScienceDirect (Core Collection) with keywords "Tidal Rice Farming," "Rice Productivity," "Technology," and "Food Security in Indonesia," and Google Scholar with keywords "Rice Production," "Tidal Swamp Rice," and "Indonesia," following the guidelines from PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The research includes peer-reviewed studies published between 2014 and 2024, focusing on the recent technology of rice farming in tidal swamp fields. The research result show Tidal swamp land in Indonesia, covering 8.92 million ha with mineral soil 7.56 million ha and peat soil 1.36 million ha, mainly used for rice farming 2.8 million ha. The current rice productivity ranges between 4-5 million tons of unhulled rice per year. This finding concludes that the adoption of innovative technologies can be applied to increase rice productivity in tidal swamps, which holds great potential for enhancing food security in Indonesia. The study also highlights the importance of additional research to address challenges such as soil salinity, water availability, accessibility, and the adoption of these technologies across different tidal swamp land conditions.

Spatio-temporal changes of rice cropping system in Anhui Province from 2010 to 2020

Monitoring changes in rice production is of significant importance for addressing food security concerns in China. However, obtaining long-term, high-resolution rice planting maps is challenging due to the influence of cloud cover and temporal resolution limitations. This study, conducted on the Google Earth Engine (GEE) platform, utilizes Landsat and Sentinel data and employs the difference in normalized difference vegetation index method (dNDVI) to extract the distribution in rice cultivation systems in Anhui Province over the past decade. The results indicate that, between 2010 and 2020, the area of single cropping rice cultivation in Anhui Province increased by 12513.612 km², while the area of double cropping rice cultivation decreased by 13527.892 km². The rice replanting index declined by 31%. With a notable shift observed in 2015, where a substantial area of double cropping rice was replaced by single cropping rice cultivation. Consequently, over the past decade, there has been a gradual decrease in the rice cultivation area in Anhui Province, accompanied by a significant shift from double to single cropping cultivation, and a continuous decline in the replanting index. This research provides vital clues for a deeper understanding of the rice planting pattern, offering scientific support for the sustainability of food production.

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

USING THE CUBIC SPLINE INTERPOLATION METHOD TO APPROXIMATE SOME REAL DATA

In engineering and science problems, the data being considered are of-ten known in the form of sets of discrete points, not as a continuous function. In applications, the values of the discrete data at specific points may be required to estimate. To solve these problems, a common mathematical ap-proach is to use the interpolation method. In this paper, we use the cubic natural spline interpolation method to build approxi-mate functions for some real data in Vietnam such as data on rice out-put and rice area of cultivation, data on expenditure and income per capita. The data used in this paper are extracted from the General Statis-tics Office of Vietnam. We develop some Matlab programs to find the coefficients of splines and calculate the values of the interpolation functions at specific points. Using the obtained interpolation function, some missing values in the data on income and expenditure per capita in the Southeast region of Vietnam in 2019 and 2021 are estimated. Fur-ther-more, some unknown data on rice productivity and cultivation area are obtained in a similar manner. A good agreement between the calcu-lated values and actual values are found.

Evaluating Water Management Efficiency in Regulating Cadmium and Arsenic Accumulation in Rice in Typical Japonica Paddy Soils at Varied pH Levels

There is growing concern regarding cadmium (Cd) exposure through rice consumption. Compared with alternate wetting and drying (AWD), continuous flooding (CF) is usually considered as an effective approach for reducing Cd enrichment in rice but increases the risk of pollution from arsenic (As). In this study, the field trial was conducted to investigate remediation effects of two water management (CF and AWD) techniques on Cd pollution in rice in typical japonica rice cultivation areas with varied soil pH levels. The results indicate that soil pH was a crucial factor in regulating CF-mediated Cd/As accumulation and migration in rice plants, and grains at all stages of rice growth. In acidic fields, compared with AWD, the use of CF reduced the accumulation of Cd in plants during the tillering stage; CF during the milk stage promotes the risk of contamination of Cd in rice grains and any form of As in plants and inhibits the content of any forms of As in grains. During the mature stage, CF reduced the levels of Cd in the plants and grains while promoting the accumulation of As(V) and total As(T-As) in plants and As(III) in grains. In alkaline fields, compared with AWD, CF during the tillering stage promoted the accumulation of various forms of As in plants. During the milk stage, CF increased and decreased the Cd content in plants and grains, respectively, and reduced the accumulation of T-As in plants and As(III) in grains; during the mature stage, CF promoted the accumulation of Cd in plants and grains, induced the accumulation of T-As plants, and inhibited the accumulation of any form of As in grains. From the perspective of food safety, the impact of CF conditions on the accumulation of Cd and As in rice from acidic fields exhibited a pattern of reduction in Cd and increase in As during the maturity period, as compared to that on the AWD. Conversely, CF increased the Cd risk while simultaneously reducing the As accumulation in rice grains to a safe level in alkaline fields. CF is not recommended as a remediation strategy for Cd pollution in rice in low Cd pollution areas, but it can be considered as a potential strategy for As pollution remediation in rice in alkaline fields with low Cd pollution.

Human coexistence with leopards and elephants: Losses and coping strategies in an Indian tiger reserve

Conflicts between humans and wildlife, particularly involving common leopards (Panthera pardus) and Asian elephants (Elephas maximus) in the Himalayan region, pose significant challenges for both communities and conservation efforts. This study fills crucial gaps in existing research, extending beyond landscape-level predictors of these conflicts. By examining patterns of losses due to leopard and elephant encounters and assessing the effectiveness of coping strategies, along with socio-economic factors associated with these conflicts in the Buxa Tiger Reserve (BTR), this study aims to inform more targeted policy approaches for forest communities. Conducting face-to-face interviews with 345 households across 10 forest villages in the BTR, the study revealed that leopards predominantly target cows during the day and goats during both day and night, while elephants raid crops, particularly during crucial agricultural phases like paddy transplantation and harvesting. Financial losses prompt the adoption of coping strategies, with traditional measures showing limited effectiveness. Visual deterrents like flashing bright LED torchlights for elephants and night lights for leopards prove more promising. Larger households with substantial livestock are more vulnerable to leopard attacks, and rice cultivation areas and the eastern division of the BTR face heightened elephant-related crop damage. The study recommends immediate measures like visual deterrents and long-term strategies such as sustainable livelihood support and habitat restoration to mitigate conflicts. Its global significance lies in advocating for tailored policies worldwide that balance conservation with livelihood protection.

Comparative Economics of Main Season and Spring Rice Production in Kanchanpur District, Nepal

Rice is the major staple food crop in Nepal which has been growing in two different seasons mainly June/July – October/November as main season rice (MSR) whereas during February/March – June/July as spring rice (SR). A comparative economic analysis of MSR and SR production in Kanchanpur district Nepal was conducted during March to July 2023. A total of 120 rice-growing farmers including 60 MSR and 60 SR growers were randomly selected from the available grower list to collect primary information by using pre-tested interview schedules. Descriptive statistics, independent sample t-tests, and the Cobb-Douglas production function were employed for the data analysis. The study finding revealed that the average landholding of MSR growers was 0.65 ha/hh, whereas the same for SR growers, was 0.55 ha/hh. The average area for MSR and SR were at 0.53 ha and 0.29 ha, respectively. The average productivity of the SR (3.69 t/ha) in the study area was found to be significantly higher than the MSR (4.61 t/ha). Despite the higher cost of production for SR (Rs. 80309.95/ha) than MSR (Rs. 75048.45/ha), the Benefit-Cost ratio for SR was found to be higher (1.61) than that of MSR (1.38). The analysis of coefficients indicated a decreasing for main season rice and increasing return to scale for spring rice, with sums of coefficients measuring 0.74 and 1.24 respectively. This implies that a 100% increase in expenditure on the variables outlined in the model for MSR and SR led to a 74% and 95% increase in the gross return of rice production. These results underscore the potential advantages of promoting improved seed varieties and adopting commercial farming practices in the spring rice cultivation area. The findings indicated the need to replace manual labor with machinery to reduce costs and increase rice cultivation profits.

Isolation and Characterization of Phosphate Solubilizing Bacteria from Upland Rice Cultivation Areas in Bangka Regency

The availability of phosphorus (P) in ultisol acid soils presents a significant challenge due to its attachment to aluminum (Al) or iron (Fe) compounds. A potential solution to address this issue is the utilization of phosphate solubilizing bacteria (PSB). Therefore, this study aimed to analyze the potential of PSB originating from upland rice cultivation on ultisol soils. The bacterial isolates were obtained from soil samples taken from the rhizosphere area and root tissue of upland rice plants cultivated in Payabenua and Saing Villages, Bangka Regency. The pathogenicity testing encompassed hypersensitivity and hemolysis tests, while the P solubilization included the evaluation of the phosphate solubilizing index (PSI) and P dissolution. Subsequently, the selected isolates were subjected to phosphatase enzyme and organic acid content assessment. The results showed a total of 120 isolates, predominantly distributed in the Payabenua area and primarily consisting of endophytic bacteria. Among the six selected isolates, genus Burkholderia dominated four isolates, while the remaining isolates belonged to genus Serratia. Furthermore, in Burkholderia vietnamiensis, the solubility value of P in AlPO4 and Ca3(PO4)2 liquid media exhibited a range of 0.0013 to 0.0344% and 0.0008 to 0.1842%, respectively.

Morphological and Genetic Diversity of Schoenoplectiella mucronata (L.) J. Jung & H. K. Choi (Ricefield Bulrush) in Rice

Since the beginning of rice cultivation, weed control has been a problem in Türkiye as well as in many other countries. Rice has both an important cultural plant and limited production for Türkiye. There are significant yield losses due to weeds and therefore weed control has an important place in rice agriculture. Species belonging to the genus Scirpus in rice production areas have recently become an important problem in rice cultivation areas of Türkiye as well as in rice cultivation areas of many other countries. In order to determine the morphological and genetic diversity of Scirpus mucronata, which is a problem in rice cultivation areas in Türkiye, 62 populations collected from the rice production areas of the Marmara and Black Sea Regions were evaluated over 8 ISSR primers and 12 morphological parameters. In the ISSR study, observed and expected heterozygosity levels ranged from 0.192 to 0.970 and from 0.136 to 0.566, respectively. In the morphological and molecular analyses performed, differences were detected in some quantitative characters between the examined populations. While morphological similarities were found between the populations grown in different regions that could not be ignored, genetic diversity was found to be higher. Morphological and genetic relationships between populations were not found to be related to geographic distance. In the context of the results, it is important to focus on field management practices such as cultural methods, as well as good control of rice seed traffic and herbicide use. It should not be forgotten that these measures are important in terms of integrated weed management strategies.

Advancing Rice Yield Forecasting and Crop Assessment in Kazakhstan’s Kyzylorda Region: A Multisource Satellite Data Approach

The monitoring and prediction of rice yields are essential across various domains, including agriculture, environmental conservation, ecology, agricultural insurance, and land resource management. The central districts of Kazakhstan’s Kyzylorda region, encompassing 70% of the total rice cultivation area, are of particular importance for ensuring national food security. This study focuses on the central Kyzylorda region, specifically the Syrdarya, Zhalagash, and Karmakshi districts, which serve as the primary hub for rice cultivation in Kazakhstan. The primary objective is to advance remote sensing techniques tailored to evaluate crop conditions and forecast rice yields, accounting for the region’s unique soil and climatic attributes. The research methodology involves comprehensive analysis of Sentinel-2 and Landsat-8 satellite data, harmonized with ground-truth information. This analysis encompasses the latest rice crop data for 2020 and precise sowing dates. Extensive field surveys were conducted to gather crucial data on growth, development, crop health, and productivity under specific agrometeorological conditions. The study provides a detailed flowchart outlining the sequential processing of remote sensing data and ground-based information, with a primary focus on forecasting rice yields in the central Kyzylorda districts for 2020. Rigorous satellite data analysis established robust correlations between the Normalized Difference Vegetation Index (NDVI) and various crop conditions, directly linked to rice yield, with high precision (R2 values ranging from 0.82 to 0.91). Validation exercises, cross-referencing satellite data with real-time field data, further enhanced accuracy. The research outcomes have extensive applications, benefiting agriculture, environmental preservation, ecology, insurance, statistics, and land management. This study underscores the significance of addressing critical challenges across various sectors through multifaceted findings.

Multivariate Assessment of Groundwater Depletion and Recharge Prospect in a Sub-tropical Basin of Eastern India

Abstract The present study aims to highlight the groundwater-stress condition of the Quaternary 一 recent alluvial region escalated by anthropogenic extraction of water resources in the western Bengal Basin (Purba Bardhaman, West Bengal, India). With the rapid development of irrigation technology, high crop yield, water- consumed rice varieties, and high cropping intensity, the farmers of the river basin go to deeper underground aquifers (>180 m bgl) to extract water for summer rice (Boro) cultivation. In this study, the five semi-critical blocks (Mangolkote, Bhatai; Manteswai; Purbasthali II, and Memari II) of Purba Bardhaman district have experienced statistically significant pre-monsoon fall (0.404 m yr- 1 to 1.099 m yr-1) and post-monsoon fall of groundwater level from 0.486 m yr-1 to 2.214 m yr-1. Principal component analysis reflects that annual irrigation draft, annual replenishable groundwater unit, net groundwater availability, Boro rice cultivation area, area to be covered on each day irrigation for Boro, and daily pump discharge required for Boro irrigation are the key factors to escalate groundwater decline. This study reveals that Galsi II, Bardhaman I and II, Katwa I, and Purbasthali I blocks will experience a water-stress or groundwater drought in future. The model of groundwater recharge potential zone shows four principal categories of groundwater prospect area in the basin, viz., poor (0.08 %), moderate (5.78 %), good (68.96 %), and very good (25.18 %). These findings provide the relevant information for planners of groundwater resources to upscale groundwater management procedures and enhance the sustainability of agricultural systems in the study area.