scholarly journals Assessing impact of saline intrusion on rice cultivating area in Ke Sach district, Soc Trang province, Vietnam

2021 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Nguyen Thanh Giao

This study was carried out to evaluate the effects of saline intrusion on agricultural activities, thereby proposing adaptation and mitigation measures for local people. Salinity was measured at 15 locations on the main and tributary rivers in Ke Sach district, Soc Trang province, Vietnam from January to September 2020. The method of direct interview with farmers was used to evaluate the knowledge and effects of saline intrusion on agricultural production. Salinity measurement results showed that salinity intrusion oc-curred and lasted from February to the end of May during the dry season (the Winter-Spring rice crop). During the rainy season (June-September), due to the large amount of water, salinity was only be-tween 0.01 and 0.1‰. However, the research has found significant effects in the Summer-Autumn rice crop due to the accumulation of salinity in the soil from the Winter-Spring rice crop, which reduces the rice yield by 10-30%. The interview results revealed that more than 80% of the farmers were aware of the effects of saline water intrusion. Local authorities also regularly organize seminars to disseminate basic knowledge to the community to minimize the risks and impacts of saltwater intrusion. The proposed temporary solutions include rainwater storage, maintenance of saline prevention sluices, construction of more water regulation canals, financial support, and strengthening of salinity monitoring stations. In order to adapt to the long-term salinity conditions, local authorities need to focus on researching changing cropping patterns, crop structure, using new rice varieties that are able to adapt to drought and salty conditions. Besides, there will be technical assistance, capital for people to adapt well to the saline intrusion situation.

2015 ◽  
Vol 34 (2) ◽  
pp. 113
Author(s):  
Sudir Sudir ◽  
Dini Yuliani ◽  
Lalu Wirajaswadi

<p>A study was carried out to identify the composition and distribution of Xanthomonas oryzae pv. oryzae (Xoo) pathotypes on rice crop in West Nusa Tenggara, during the 2012 planting season. Three activities were conducted, namely collection of rice leaf samples from the fields, isolation of Xoo from the leaf samples at the laboratory, and testing pathotypes of Xoo at the screen house. Rice leaves showing typical bacterial leaf blight (BLB) symptom were collected from various farmers’ fields. The samples were detached and put into paper envelopes, and were taken to the laboratory for isolation of Xoo, at the Laboratory of Pythopathology of Indonesian Center for Rice Research (ICRR), Sukamandi. Pathotype testing was done in the ICRR screen house by inoculating the leaves of five differential rice varieties using inocula of the Xoo isolates. Resistance of the rice differential varieties was determined based on the BLB disease severity. Inoculated plant with disease severity ≤11% was considered resistant (R) and disease severity &gt;11% was susceptible (S). From the 240 samples of rice leaf infected with BLB collected from West Nusa Tenggara, 232 Xoo isolates were obtained. The Xoo pathotype identification showed that pathotype IV was the most dominant in West Nusa Tenggara during the 2012 planting season, numbering 118 isolates or 51.0% out of the total isolates, followed by pathotype VIII (67 isolates or 29.0%), and pathotype III (47 isolates or 20.0%).</p>


2018 ◽  
Vol 10 (2) ◽  
pp. 15-31 ◽  
Author(s):  
James W. Baurley ◽  
Arif Budiarto ◽  
Muhamad Fitra Kacamarga ◽  
Bens Pardamean

High quality models of factors influencing rice crop yield are needed in countries where rice is a staple food. These models can help select optimal rice varieties for expected field conditions. Development of a system to help scientist track and make decisions using this data is challenging. It involves incorporation of complex data structures - genomic, phenotypic, and remote sensing - with computationally intensive statistical modeling. In this article, the authors present a web portal designed to help researchers to manage and analyze their datasets, apply machine learning to detect how factors taken together influence crop production, and summarize the results to help scientists make decisions based on the learned models. The authors developed the system to be easily accessed by the entire team including rice scientist, genetics, and farmers. As such, they developed a system on a server architecture comprised of a SQLite database, a web interface developed in Python, the Celery job scheduler, and statistical computing in R.


2020 ◽  
Vol 12 (7) ◽  
pp. 1186 ◽  
Author(s):  
A.-M. Olteanu-Raimond ◽  
L. See ◽  
M. Schultz ◽  
G. Foody ◽  
M. Riffler ◽  
...  

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.


Author(s):  
D. N. Jagtap ◽  
U. V. Mahadkar ◽  
S. A. Chavan

A field experiment was conducted during kharif season 2015-16 to study the response of rice varieties to different sowing windows under Konkan conditions. The experiment was laid out in split plot design with three replications. The main plot treatments were three sowing windows, viz., 23rd Met Week, 24th Met Week, 25thMet Week. The sub plot treatments comprised five rice varietiesviz., Karjat-5, Palghar-1, Jaya, Swarna and Karjat-2. Thus there were 45 treatment combinations. Results revealed that rice crop sown on 23rd Met. Week recorded significantly higher grain yield (5782 kg ha-1) and straw yield (6462 kg ha-1). Long duration rice variety Swarna recorded the maximum grain yield (5782 kg ha-1) as well as straw yield (6462 kg ha-1), which was significantly higher over all other varieties under study except variety Jaya which was at par. From the present investigation it can be concluded that kharif rice in Konkan be sown during 23rd meteorological week with rice variety Swarna followed by conventional variety Jaya, so as to obtain higher yield and economic returns


2020 ◽  
Vol 2 (1) ◽  
pp. p95
Author(s):  
Mohammad Ashiqur Rahman ◽  
Tanvir Ahmed ◽  
Mohammad Abdul Mojid

Irrigation with saline water adversely affects rice production and degrades land productivity in the coastal zones of many countries in the world. This study aimed at developing a suitable irrigation management practice to reduce the harmful effects of salinity on rice production under saline water irrigation. An experiment in raise-bed lysimeters was set in a split-split-plot design with irrigation–drainage practice as the main factor, irrigation water salinity as the sub-factor and rice variety as sub-sub factor; main factor and sub-factor comprised four treatments and the sub-sub factor comprised three treatments, each with three replications. The treatments of the main factor were – T1: 2-5 cm continuous ponding, T2: continuous saturation, T3: changing irrigation water after 3 days of application by maintaining 2-5 cm ponding depth, and T4: changing irrigation water after 5 days of application by maintaining 2-5 cm ponding depth. The sub-factor comprised – SL1: fresh water as control, SL2: saline water of 6 dS m-1, SL3: saline water of 9 dS m-1, and SL4: saline water of 12 dS m-1. The sub-sub factor comprised three salt-tolerant rice varieties V1: Binadhan-8, V2: Binadhan-10, and V3: BRRI dhan-47. The irrigation–drainage practices T2 and T3 provided significantly (p£0.05) improved growth and yield attributes of the rice varieties under salinity water level SL3 and SL4 compared to T1 and T4 treatments. The treatment T3 maintained least exposure of the crop to high degree of salinity and produced satisfactory plant attributes by inhibiting the detrimental effects of salinity. Therefore, T3 is suggested for adoption in practical fields when provision for removing high saline water from the rice fields can be arranged.


2022 ◽  
Vol 964 (1) ◽  
pp. 012019
Author(s):  
Nguyen Thi Hong Diep ◽  
Phan Ky Trung ◽  
Dinh Thi Cam Nhung ◽  
Nguyen Thi Thanh Huong ◽  
Pham Thanh Vu ◽  
...  

Abstract This study aimed to evaluate the adaptive capacity (AC) of agriculture farming transformation to improve for the adaptability by saline intrusion of those culture systems in the coastal Ben Tre province. The primary data was collected by questionnaires in three coastal districts of Ba Tri, Thanh Phu and Binh Dai with 178 households to distribute on agriculture land use transformations. An Object-Based Image Analysis (OBIA) and multi-temporal image analysis approach was developed to detect how LULC changes during 2010-2020 in the coastal Ben Tre province using Landsat TM and OLI data. The MODIS (MOD09 8-day reflectance) data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common rice crops LULC types using unsupervised classification by ISODATA algorithm. Based on primary data collection, evaluating adaptation measures was assessed the compilation of modification options based on a detailed description and criteria including human, society, infrastructure and natural conditions by standardized a weight ranges of adaptive indicators in the agriculture land use changes. The results detected five agriculture land use transformation including rice-shrimp crop rotation to aquaculture, single rice crop to aquaculture, double rice crop to perennial plant/orchards, double rice crop to rice-shrimp crop rotation, and triple rice crop to double rice crop. The adaptability of these transformations conducted the shrimp-rice crop rotation to aquaculture in Binh Dai of strongly high adaptability. Next is followed by double rice crops to orchards and double rice crops to rice-shrimp crop rotation with high adaptability while the rice-shrimp crop rotation to aquaculture is only moderate in Thanh Phu. The low adaptability levels were identified on single rice crop to aquaculture and triple rice crop to double rice crop transformations due to tolerant condition of saltwater for rice crop cultivation. The studies results can further contribute to support adaptation planning in these sectors by using, developing and streamlining this framework to additional and different socio-ecological contexts.


Author(s):  
Stefano De Leo

AbstractAs the number of Covid-19 infections worldwide overtakes 6 millions of Total Confirmed Cases (TCC), the data reveal almost closed outbreaks in many European countries. Using the European data as a basis for our analysis, we study the spreading rate of Covid-19 and model the Daily Confirmed Cases and Deaths per Million (DCCpM and DDpM) curves by using “skew-normal” probability density functions. The use of these asymmetrical distributions allows to get a more realistic prediction of the end of the disease in each country and to evaluate the effectiveness of the local authorities strategies in facing the European outbreak. The initial stage of the Brazilian disease is compared with the early phase of the European one. This is done by using the weekly spreading rate of Covid-19. For Sweden, UK, and USA, we shall give a forecast for the end of pandemic and for Brazil the prediction of the peak of DDpM. We also discuss additional factors that could play an important role in the fight against Covid-19, such as the fast response of the local authorities, the testing strategies, the number of beds in the intensive care units, and, last but not least, the measures of isolation adopted. The Brazilian mitigation measures can be placed between the strict lockdown of many European countries and the Swedish approach, but clearly much comparable to the European ones (in particular to the Netherlands).MethodsFor Brazil, the weekly spreading rates of Covid-19, as more people are getting infected, was used to compare the outbreak in these countries with the ones of the European countries when they were at the same stage of infection. In the early stage of the disease, normal distributions have been used to obtain what we call a dynamic prediction of the peaks. After reaching the peak of daily infections and/or deaths, skew-normal distributions are required to correctly fit the asymmetrical DCCpM and DDpM curves and get a realistic forecast of the pandemic end.FindingsThe European data analysis shows that the spreading rate of Covid-19 increased similarly for all countries in its initial stage, but it changed as the number of TCCpM in each country grew. This was caused by the different timely action of the authorities in adopting isolation measures and/or massive testing strategies. The early stage of the outbreak in the USA and Brazil shows for their α factor (DCCpM) a behaviour similar to Italy and Sweden, respectively. For the β factor (DDpM), the American spreading is similar to the one of Switzerland, whereas the Brazilian factor is greater than the ones of Portugal, Germany, and Austria (which showed, in terms of TDpM, the best results in Europe) but, at the moment, it is lower than the other European countries.InterpretationThe fitting skew parameters used to model the DCCpM and DDpM curves allow a more realistic prediction of the end of the pandemic and give us the possibility to compare the mitigation measures adopted by the local authorities by analysing their respective skew normal parameters (mean, mode, standard deviation, and skewness). In Europe, Sweden and the UK show the greatest asymmetries, a kind of marathon instead of the sprint of other European countries (as observed by Swedish authorities). This also happens for the USA. The Brazilian weekly spreading rate for deaths is lower than most of the European countries at the same stage of the outbreak.FundingIndividual grants by CNPq (2018/303911) and Fapesp (2019/06382–9).


2020 ◽  
Author(s):  
Stefano De Leo

BACKGROUND As the number of Covid-19 infections worldwide overtakes 6 millions of Total Confirmed Cases (TCC), the data reveal almost closed outbreaks in many European countries. Using the European data as a basis for our analysis, we study the spreading rate of Covid-19 and model the Daily Confirmed Cases and Deaths per Million (DCCpM and DDpM) curves by using ``skew-normal'' probability density functions. OBJECTIVE The use of these asymmetrical distributions allows to get a more realistic prediction of the end of the disease in each country and to evaluate the effectiveness of the local authorities strategies in facing the European outbreak. The initial stage of the Brazilian disease is compared with the early phase of the European one. This is done by using the weekly spreading rate of Covid-19. For Sweden, UK, and USA, we shall give a forecast for the end of pandemic and for Brazil the prediction of the peak of DDpM. We also discuss additional factors that could play an important role in the fight against Covid-19, such as the fast response of the local authorities, the testing strategies, the number of beds in the intensive care units, and, last but not least, the measures of isolation adopted. The Brazilian mitigation measures can be placed between the strict lockdown of many European countries and the Swedish approach, but clearly much comparable to the European ones (in particular to the Netherlands). METHODS For Brazil, the weekly spreading rates of Covid-19, as more people are getting infected, was used to compare the outbreak in these countries with the ones of the European countries when they were at the same stage of infection. In the early stage of the disease, normal distributions have been used to obtain what we call a dynamic prediction of the peaks. After reaching the peak of daily infections and/or deaths, skew-normal distributions are required to correctly fit the asymmetrical DCCpM and DDpM curves and get a realistic forecast of the pandemic end. RESULTS The European data analysis shows that the spreading rate of Covid-19 increased similarly for all countries in its initial stage, but it changed as the number of TCCpM in each country grew. This was caused by the different timely action of the authorities in adopting isolation measures and/or massive testing strategies. The early stage of the outbreak in the USA and Brazil shows for their $\boldsymbol{\alpha}$ factor (DCCpM) a behaviour similar to Italy and Sweden, respectively. For the $\boldsymbol{\beta}$ factor (DDpM), the American spreading is similar to the one of Switzerland, whereas the Brazilian factor is greater than the ones of Portugal, Germany, and Austria (which showed, in terms of TDpM, the best results in Europe) but, at the moment, it is lower than the other European countries. CONCLUSIONS The fitting skew parameters used to model the DCCpM and DDpM curves allow a more realistic prediction of the end of the pandemic and give us the possibility to compare the mitigation measures adopted by the local authorities by analysing their respective skew normal parameters (mean, mode, standard deviation, and skewness). In Europe, Sweden and the UK show the greatest asymmetries, a kind of marathon instead of the sprint of other European countries (as observed by Swedish authorities). This also happens for the USA. The Brazilian weekly spreading rate for deaths is lower than most of the European countries at the same stage of the outbreak.


2021 ◽  
Vol 2 (1) ◽  
pp. 13-22
Author(s):  
Le Ngoc Nuong ◽  
Ngo Thi Huyen Trang ◽  
Nguyen Thi Thuy Trang ◽  
Nguyen Tien Lam

Tourism development policy will pay attention to stakeholders, especially the people, who play an important role. However, in reality, when people participate in rural tourism development, they are not equipped with basic knowledge and skills in managing and serving tourists. Because the benefits between stakeholders are still not clear, the participation of people in rural tourism development is currently only passive. Active participation need to be directed from the Government and local authorities because the concept that rural tourism development is not only about creating economic benefits but also associated with conservation and preservation, together with keeping and promoting cultural - historical values in rural areas.


1970 ◽  
Vol 8 (2) ◽  
pp. 88-97 ◽  
Author(s):  
M Shahe Alam ◽  
MA Quayum ◽  
MA Islam

A study was conducted in the haor areas of Bangladesh to assess the land utilization status, delineate the productivity and profitability of growing modern rice, evaluate the existing cropping patterns and assess the prospect of possible cropping patterns. Both primary and secondary data were used in the study. Applying the conventional descriptive statistics, the study revealed that, there are about 1.26 million hectares of cultivated lands in seven haor districts, of which 66% falls under haor area. In Kishoregonj and Hobigonj, nearly 94 and 87% areas were devoted to MV Boro rice production. Both BRRI dhan 28 and 29 were being the widely adopted rice varieties. On an average, about 33% of the haor areas were under mechanized irrigation, but in Kishoregonj, the coverage of mechanized irrigation was 87% that helped increasing cropping intensity. The cost of production for MV Boro was almost double than that of LV rice. The yield of MV Boro was 79% higher than that of LVs and the return from MVs was 82% higher. Rabi-Fallow-T. Aman, Vegetable-Aus-T. Aman and Rabi-B.Aman patterns were the potential cropping patterns in some selected areas and this could increase both cropping intensity and productivity in those areas. According to the farmers' assessment, lack of flood control dam and lack of short duration varieties etc. are the major hindrance to the adoption of potential cropping patterns. Construction of community harvest and threshing facilities and flood control devices could be the important public interventions for enhanced agricultural productivity in the haor areas. Key words: Haor areas; potential pattern; productivity; mechanization; profitability DOI: 10.3329/agric.v8i2.7582 The Agriculturists 8(2): 88-97 (2010)


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