• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.413-433
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• 2010

South Dakota is an important agricultural state in the United States with annual cash receipts from agricultural products exceeding $9 billion dollars. This production is possible because of large areas of productive soils. This publication describes the general characteristics and qualities of the major soil groups recognized in South Dakota. The soil forming factors are briefly described, soil classification is introduced, and the genesis of typical Udalf and Ustoll soils are discussed. Soil management issues impacting the use of SD soils are considered. Long-term (>70 yrs) cultivation has significantly reduced surface soil organic carbon levels (>30% reduction) when compared to non-cultivated soil. Soil test phosphorus levels significantly increased in cultivated fields due to commercial P fertilization. The major long-term production problems for SD soils are conservation of soil moisture, organic matter and nitrogen losses, fertility management, and wind and water erosion control.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.3
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• pp.331-336
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• 2011

Capacitance soil moisture sensor is extensively used by soil research and irrigation management with its convenience and accuracy. This experiment was conducted to evaluate the acceptability of capacitance soil moisture sensor, named EnviroSCAN made by Sentek Ltd., in Jeju Island where volcanic ash soils are widely distributed, and to calibrate it to various soils with different amount of soil organic matter. For sensor calibration equation of volcanic ash soils, a logarithm function is better than a typical power function of non-volcanic ash soils. So there are possibilities of under evaluated in soil water contents in very wet and very dry conditions by using typical power function with volcanic ash soil areas. We suggested practical coefficients of typical calibration equation for using capacitance sensor in volcanic ash soils, also suggested equations for estimation of them with soil organic matter contents. The measurement of soil water content with a capacitance sensor can be affected by some soil characteristics such as porosity, soil organic matter content, EC, etc. So those factors should be controlled for improving the accuracy of measurement.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.184-185
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• 2005

• Asian Journal of Turfgrass Science
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• v.25 no.2
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• pp.223-228
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• 2011

This research was performed to investigate change of green speed according to growth of grass, for this, the method of effective green management for improvement of green speed was searched by investigating green density, soil moisture, surface hardness, and mowing height every day for 6 months. As the result of the study, reliability between, green density, soil moisture, surface hardness, mowing height and green speed were measured to be respectively 0.4742, 0.5690, 0.4632, 0.2806, i.e. soil moisture is considered as the factor which affects green speed the most. Therefore, it will be an advantageous environment to maintain soil moisture a little bit low to improve green speed within the range that does not disrupt the growth of green. In case of green density, it is considered to be effective to get a fast green speed when obtaining enough density during May~June, the most vigorous growth period and at the same time green up period. Surface hardness was confirmed that management work as rolling is a considerably effective method to increase hardness. However, rolling gives high stress to the green, combining another management work as regular hilling could be a good alternative. Reliability of green preview and green speed was 0.2806, lower than soil moisture or surface hardness. Through the results, it was confirmed that management of mowing height to be low less than 3.00 mm is helpful to improve green speed, timely, and it is advantageous to manage green speed when adjusting mowing height during the vigorous growth period of bent grass. However, considering the range of mowing height was not various, being 2.9~3.4 mm, henceforth research on investigation of green speed at more various mowing heights would be necessary. Consequently, except mowing height, other three factors, i.e. green density, soil moisture and surface hardness were investigated to have considerable level of reliability on green speed, and it is considered that each factor affects green speed respectively according to green condition and time. Accordingly, in order for the manager to maintain high speed all year round, intensive care for each factor per time unit considering green growth condition is considered to be necessary.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.280-291
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• 2021

The study examined the influence of climate and soil dynamics on vegetation health across the ecological zones in Rivers State, Nigeria. MODIS imagery was used to assess the vegetation health through NDVI and point grid pattern of meteorological data for total precipitation (TP), air temperature (AT), soil moisture (SM) and soil temperature (ST) of 2000, 2003, 2006, 2009, 2012 and 2015 were used for the study. Descriptive and inferential statistics were used for data analysis. Findings showed that NDVI ranged between 0.420 and 0.612 in the freshwater swamp (FWS) while between 0.465 and 0.611 in the rainforest and the NDVI in the mangrove was generally low. The highest mean AT was experienced in the mangrove ecological zone and the least was experienced in the rainforest. The mean SM was generally highest in the rainforest with highest value in 2000 (774.44 m³/m³). The ST was highest in the mangrove and the least was experienced in the rainforest while the TP was highest in the mangrove. NDVI correlated significantly with SM (r=0.720; p<0.05) and ST (r= -0.493; p<0.05). NDVI, SM, TP and ST significantly varied among the ecological zones. Regression analysis showed that vegetation health was significantly related to the combination of soil temperature and soil moisture (R²=0.641; p=0.000). Thus, monitoring the factors that affect vegetation health in a changing climate and soil environments is highly required.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.121-130
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• 2019

The purpose of this study is to determine the amount of irrigation water for upland crop growth based on the 30 year of historical rainfall data and the water management guidelines as a reference. Five regions and ten crops were selected by their cultivation size. The changes of soil moisture contents were calculated using daily mean rainfall and irrigation demand. This study assumed that crops are irrigated when the soil moisture contents fell below of the field capacity for more than 5 days, which is the drought condition defined by RDA. The maximum irrigation water requirements was 167.2 mm for chinese cabbage during the growing season, which was followed by corn (112.0 mm), daikon (102.3 mm), spinach (66.1 mm), lettuce (56.7 mm), pepper (46.5 mm), potato (33.9 mm), sweet tomato (27.4 mm), peanut (11.5 mm) and bean (10.3 mm), The results of this study could contribute to providing valuable data to determine the capacity of irrigation facilities and to establish the emergency operation plans under extreme unfavorable weather condition (heat wave, etc.) for crop growth.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.37-47
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• 2017

In this study, we analyzed the variability of irrigation water amounts based on the combination of various crops and soil textures using the Irrigation Water Management Model (IWMM). IWMM evaluates the degree of agricultural drought using the Soil Moisture Deficit Index (SMDI). When crops are damaged by the water scarcity under the drought condition indicating that the SMDI values are in negative (SMDI<0), IWMM irrigates appropriate water amounts that can shift the negative SMDI values to "0" to crop fields. To test the IWMM model, we selected the Bandong-ri (BDR) and Jucheon (JC) sites in Gangwon-do and Jeollabuk-do provinces. We derived the soil hydraulic properties using the near-surface data assimilation scheme form the Time Domain Reflectrometry (TDR)-based soil moisture measurements. The daily root zone soil moisture dynamics (R: 0.792/0.588 and RMSE: 0.013/0.018 for BDR/JC) estimated by the derived soil parameters were matched well with the TDR-based measurements for validation. During the long-term (2001~2015) period, IWMM irrigated the minimum water amounts to crop fields, while there were no irrigation events during the rainy days. Also, Sandy Loam (SL) and Silt (Si) soils require more irrigation water amounts than others, while the irrigation water were higher in the order of radish, wheat, soybean, and potato, respectively. Thus, the IWMM model can provide efficient irrigation water amounts to crop fields and be useful for regions at where limited water resources are available.

• Journal of Biosystems Engineering
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• v.33 no.2
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• pp.115-123
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• 2008

This study was conducted to develop an automatic soil water content control system for greenhouse, which consisted of drip irrigation nozzles, soil water content sensors, an on/off valve, a servo-motor assembly and a control program. The control logic adopted in the system was Ziegler-Nichols algorithm and rising time, time constant and over/undershoot ratio as control variables in the system was selected and determined by various control experiments to maintain small delay time and low overshoot. Based on the experimental results, it was concluded that the control system developed in the study could replace the unreliable conventional greenhouse soil water management.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.5
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• pp.333-342
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• 2013

Soil compaction is affected by soil texture, organic matter (OM), strength (ST) and soil moisture, which is difficult to understand the degree and effects of related factors. The purpose of the study is to assess the impact of them on the compaction with bulk density (BD). The analysis was conducted with data collected from national-wide monitoring sites including 105 upland soils, 246 orchard soils, and 408 paddy soils between 2009 and 2012. The distributions of soil physical properties were measured. The correlation and multi linear regression analysis were performed between soil physical properties using SAS. The regression equation of BD(y) includes ST, gravitational water contents (GWC), and OM as variables commonly, having additional factors, clay content and sand content in paddy soil and upland soil for only subsoil (p<0.001). Our results show that the BD could be explained about 40~50% by various physical properties. The regression was mainly determined by ST in orchard and upland soil and by the GWC in paddy soil. To mitigate soil compaction, it is important to maintain the proper level of OM in upland soil and to consider the moisture condition with soil texture in paddy soil when making work plan. Furthermore, it would be recommended the management criteria classified by soil texture for the paddy soils.