scholarly journals A Study of the Spatial Difference of the Soil Quality of The Mun River Basin during the Rainy Season

2019 ◽  
Vol 11 (12) ◽  
pp. 3423 ◽  
Author(s):  
Chunsheng Wu ◽  
Qingsheng Liu ◽  
Guoxia Ma ◽  
Gaohuan Liu ◽  
Fang Yu ◽  
...  
Keyword(s):  
Soil Quality ◽  
River Basin ◽  
Rainy Season ◽  
Crop Production ◽  
Soil Condition ◽  
Soil Conditions ◽  
Eastern Region ◽  
Data Set ◽  
Main Period

The Mun River basin is one of the main grain-producing areas of Thailand, and the rainy season is the main period for crop planting after being idle during the dry season. However, the soil conditions are variable, so an assessment of soil quality during the rainy season is necessary for improving soil condition and crop production. The aim of this study was to conduct a soil quality assessment based on soil samples. To attain that, a minimum data set theory was used to screen evaluation indicators and geographically weighted regression was performed to obtain spatial interpolations of indicators, while the fuzzy logic model was used to determine the soil quality results. The results showed that the contents of indicators had similar spatial trends as their contents declined from the western to the eastern region of the basin. The soil quality results showed that the poor soil was in the middle of the basin, where the main land use is paddy fields, and the good soil was in the southwest of the basin, where forests and dry fields are widely distributed. The results indicated that the soil quality in the Mun River basin varied greatly, especially for farmland, so these findings will be helpful for improving soil conditions and grain production in the Mun River basin.

scholarly journals Soil-Quality Assessment during the Dry Season in the Mun River Basin Thailand

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Chunsheng Wu ◽  
Erfu Dai ◽  
Zhonghe Zhao ◽  
Youxiao Wang ◽  
Gaohuan Liu
Keyword(s):  
Quality Assessment ◽  
Soil Quality ◽  
River Basin ◽  
Soil Nutrient ◽  
Dry Season ◽  
Soil Conditions ◽  
Data Set ◽  
Soil Quality Assessment ◽  
The Status ◽  
Better Than

The Mun River Basin is one of Thailand’s major grain-producing areas, but the production is insufficient, and most of the cultivated lands are rain-fed and always unused in the dry season. All this makes it necessary to determine the status of soil nutrients and soil quality in the dry season to improve soil conditions, which will be useful for cultivation in the farming period. The aim of this study was to construct a soil-quality assessment based on soil samples, and in the process the minimum data set theory was introduced to screen the assessment indicators. The geographically weighted regression method was used to complete the spatial interpolation process of indicators, and the fuzzy logic model was constructed to evaluate the soil quality. The results showed that the spatial distributions of soil quality and indicators were similar. The soil quality was the best in the upstream while poor in the downstream, and the dry fields in the west and the forests in the east of the basin were better than other areas nearby. However; the soil qualities of paddy fields in the middle and east of the basin were poor due to the lack of soil nutrient supply when the fields were unused

scholarly journals Evaluasi Kualitas Tanah Sawah Intensif dan Sawah yang Dikonversikan untuk Kebun di Subak Kesiut Kerambitan Tabanan

2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
I DEWA MADE ARTHAGAMA ◽  
I MADE DANA
Keyword(s):  
Soil Quality ◽  
Paddy Soil ◽  
Field Survey ◽  
Research Area ◽  
Paddy Soils ◽  
Minimum Data Set ◽  
Data Set ◽  
Minimum Data ◽  
Better Than

 Evaluation Quality of Intensif Paddy Soils and Conversion Paddy Soils to Garden at Subak Kesiut Kerambitan Tabanan. This experiment conducted to evaluate intensif Paddy Soils and conversion paddy soils to garden at SubakKesiut Kerambitan Tabanan. There were two steps applied in this study including field survey to determine the research area and points soils sampling; analysis soils properties are: physic, chemistry and soils biology for get minimum data set to determine the soil quality at Laboratory of Soils and Enveronment Faculty of Agriculture Unud. The results of this study showed: the soil quality of intensif paddy soils is better than conversion paddy soils to garden, that showed with SQR at intensif paddy soil is 18 and at conversions paddy soil is 25. The limiting faktor at conversion paddy soils to garden is P available, there are less than at intensif paddy soils.

scholarly journals Rainfall and Agricultural Production in Botswana

Afrika Focus ◽  
1990 ◽  
Vol 6 (2) ◽  
pp. 141-155
Author(s):  
Paul Vossen
Keyword(s):  
Crop Yield ◽  
Agricultural Production ◽  
Rainy Season ◽  
Crop Production ◽  
Time Trend ◽  
Quality Monitoring ◽  
Production Forecasting ◽  
Positive Time ◽  
Semi Arid

The interannual variability of traditional, rainfed agricultural production of Botswana, a country with a typical semi-arid climate, is almost completely accounted for by the quality of the rainy season. It appears that the variability of the national cattle death ratio, total planted area and crop yield are, for more than 95% accounted for by rainy season conditions. As a result, also the nutritional state of the population highly correlates with rainfall. Despite the severe droughts of 1978/79and1985/86, farmers were not discouraged to practice agriculture: in fact, crop production shows a significant positive time trend which becomes apparent, when the trend and the rainy season conditions are analysed in combination with each other. As part of this study, models were developed and validated for a precise and areawise agricultural rainy season quality monitoring and for national agricultural production forecasting in Botswana. One of these models could possibly also be used for the areawise assessment of risks for malnutrition of children under five years old.

scholarly journals Soil Quality at Rice Fields with Organic, Semi-organic and Inorganic Management in Wonogiri Regency, Indonesia

2021 ◽  
Vol 36 (2) ◽  
pp. 259
Author(s):  
Supriyadi Supriyadi ◽  
Intan Lestari Prima Vera ◽  
Purwanto Purwanto
Keyword(s):  
Soil Quality ◽  
Rice Field ◽  
Public Awareness ◽  
Pearson Correlation ◽  
Principal Component ◽  
Rice Fields ◽  
Soil Conditions ◽  
Soil Quality Index ◽  
Data Set ◽  
Organic Rice

The high demand of rice is fulfilled by intensification, particularly with the use of chemical fertilizer that allegedly causes land and environmental problems in a long term. As public awareness of environmental health rises, more rice fields are managed organically and semi-organically, but there are still many that manage rice fields inorganically. Assessment of soil quality of the three types of rice field management is important to prove that organic rice fields have better soil quality than semi-organic and inorganic rice fields, as well as to evaluate soil conditions on the location. This research was conducted in Girimarto, Wonogiri, Indonesia, using a descriptive explorative method with a survey approach on three points of each management system of rice fields, which are organic, semi-organic and inorganic rice fields. Statistical analysis was performed by Pearson correlation analysis and principal component analysis (PCA) to determine the indicators affecting soil quality, which are called the minimum data set (MDS). There were selected indicators in this research, including total microbes, base saturation, cation exchangeable capacity and organic carbon. Based on the results of the study, organic rice fields have the best soil quality with a score of soil quality index (SQI) of 2.3, compared to semi-organic rice field SQI (2.2) and inorganic rice field SQI (1.7). The results indicate that organic management contributes to better soil quality and environment.

scholarly journals Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Agriculture ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 469
Author(s):  
Kamal Nabiollahi ◽  
Eskandari Heshmat ◽  
Amir Mosavi ◽  
Ruth Kerry ◽  
Mojtaba Zeraatpisheh ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Soil Quality ◽  
Agricultural Land ◽  
Crop Yields ◽  
Wheat Yield ◽  
Data Set ◽  
Study Region ◽  
Soil Quality Assessment ◽  
Rainfed Wheat

Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA), and multiple linear regression (MLR) are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0–20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Wheat grain yield for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators determined for the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQIw-M) and the rainfed wheat yield (r = 0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQIw-F) (r = 0.58). Results showed that the means of both SQIw-M and SQIw-F and rainfed wheat yield for Inceptisols were higher than for Entisols, although these differences were not statistically significant. Both SQIw-M and SQIw-F showed that areas with Entisols had lower proportions of good soil quality grades (Grades I and II), and higher proportions of poor soil quality grades (Grades IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientifically integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields should be promoted.

scholarly journals Anthropic influence in the water quality of the river basin that supplies the Pato Branco Municipality-PR

2009 ◽  
Vol 52 (1) ◽  
pp. 221-232 ◽  
Author(s):  
William César Polônio Machado ◽  
Valter Antonio Becegato ◽  
André Virmond Lima Bittencourt
Keyword(s):  
River Basin ◽  
Height Difference ◽  
Data Set ◽  
Total N ◽  
Physico Chemical ◽  
Anthropic Influence ◽  
Hydrological Cycles ◽  
Nitrate And Nitrite ◽  
Monitoring Stations

The Pato Branco river basin, which supplies water to Pato Branco city (70,000 inhabitants) in southwestern Paraná State, was monitored during two hydrological cycles, totaling 22 campaigns at two monitoring stations on the Conrado river: the Conrado station upstream (RC01) and the Conrado station downstream (RC02). The main characteristics of the basin here: area - 29.69 km², length - 9,300 m, height difference - 170 m, declivity - 18.62 m km-1, and concentration time - 102 min. Physico-chemical variables were analyzed, using samples collected after a minimum three-day period without rain. The data set comprising seven variables yielded the following averages respectively for monitoring stations RC01 and RC02: variables that characterized the organic matter: BOD5 (5.2 - 4.31mg L-1 O2) and COD (11.04 - 11.2 L-1 O2), and variables that characterized the eutrophication processes: ammoniacal N (0.16 - 0.09 mg L-1 N); nitrate (0.67 - 0.78 mg L-1 N); nitrite (0.06 - 0.029 mg L-1 N); total N (0.48 - 0.33 mg L-1 N), and total phosphorous (0.13 - 0.07 mg L-1). The lowest and the highest variation coefficients for data downstream and upstream corresponded to: nitrate and nitrite (46.1 and 166%); phosphorous and nitrite (56.8 and 229.6%).

Using seasonal forecast information to strengthen resilience and improve food security in Niger River Basin

2020 ◽  
Author(s):  
Bernard Minoungou ◽  
Jafet Andersson ◽  
Abdou Ali ◽  
Mohamed Hamatan
Keyword(s):  
Food Security ◽  
West Africa ◽  
River Basin ◽  
Rainy Season ◽  
Hydrological Model ◽  
Seasonal Forecast ◽  
Reference Period ◽  
Seasonal Forecasts ◽  
Niger River

<p>The rainy season occupies a central place in socio-economic activities in the Sahelian regions, as more than 80% of the population lives on agriculture and livestock. However, extreme hydroclimatic events such as droughts and floods affect these activities. Efforts made in recent years in the production of hydroclimatic information to enhance the resilience of populations have become insufficient, given the variability and climate change.</p><p>In this context, we have conducted a study on improving the quality of seasonal forecast information to strengthen the resilience and improve the food security in West Africa, especially on the Niger River Basin. We used seasonal climate forecasts and the HYPE hydrological model to predict some characteristics of the rainy season in West Africa. The ECMWF seasonal forecast ensemble (system 5) from 1993 to 2015 (hindcast) and 2018 (forecast), available in the Climate Data Store (CDS) catalogue were used. The climatic variables considered are daily precipitation, mean and extreme temperatures (minimum and maximum) at the seasonal scale. The main objective was to assess the ability of the HYPE hydrological model, developed by Swedish Meteorological and Hydrological Institue, to predict runoff over the historical period and to produce hydrological seasonal forecasts for next years.</p><p>The main season’s characteristics produced are: (i) cumulative rainfall map for the rainy season (May to November), (ii) the rainfall situation of the season (above, near or below normal considering 1993-2015 as reference period), (iii) hydrological situation of the season (above, near or below normal considering 1993-2015 as reference period), (v) graph of the mean seasonal streamflow over the Niger Basin compared to the reference period (1993-2015).</p><p>The predictability of 2018 hydrological seasonal products were assessed and the results are promising. The main challenges we faced were the initialisation of the model, the bias correction (the reference data to be considered and the appropriate method). Further research on these topics should continue to improve the quality of results.</p>

CROPPING SYSTEM EFFECTS ON SOIL QUALITY FOR THREE AGRO-ECOSYSTEMS IN INDIA

2013 ◽  
Vol 50 (3) ◽  
pp. 321-342 ◽  
Author(s):  
NISHANT K. SINHA ◽  
USHA KIRAN CHOPRA ◽  
ANIL KUMAR SINGH
Keyword(s):  
Soil Quality ◽  
Crop Production ◽  
Cropping Systems ◽  
Soil Layer ◽  
Cropping System ◽  
Scoring Functions ◽  
Soil Quality Index ◽  
Data Set ◽  
Non Linear ◽  
Semi Arid

SUMMARYSoil quality integrates the effects of soil physical, chemical and biological attributes. Some of them are dynamic in nature and behave differentially in various agro-ecosystems (AESs) and are quantified in terms of a soil quality index (SQI). An attempt has been made in this paper to develop an SQI based on a minimum data set (MDS), which could be used to evaluate the sustainability of the crop production in three varying AESs in India, namely sub-humid, semi-arid and arid. Thirteen indicators were utilized to develop the SQI from the properties measured from the surface soil layer (0–15 cm). Each indicator of the MDS was transformed into a dimensionless score based on scoring functions (linear and non-linear) and integrated into four SQIs. The weighted non-linear index (WNLI) was identified as the most sensitive for all the AESs and was recommended as an index for future assessments. Based on this index, the quantification of soil quality under several cropping systems was carried out for sub-humid, semi-arid and arid AESs and the most suitable cropping system was identified. WLNI was positively and significantly correlated (R2= 0.79,p< 0.01) with wheat equivalent yield for all the cropping systems. This clearly indicated that the index may be used satisfactorily for quantifying soil quality.

scholarly journals Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Jeffrey L. Smith ◽  
Jonathan J. Halvorson
Keyword(s):  
Spatial Variability ◽  
Soil Quality ◽  
Crop Production ◽  
Arable Land ◽  
Microbial Biomass C ◽  
Soil Conditions ◽  
Spatial And Temporal Variability ◽  
Biological Parameters ◽  
Field Scale ◽  
Soil Quality Index

Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK) procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO2> microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

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