• 한국농공학회논문집
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• 제58권5호
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• pp.81-90
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• 2016

Due to droughts and water shortages causing severe damage to crops and other vegetations, much attention has been given to efficient irrigation for upland farming. However, little information has been known to measure soil moisture levels in a field scale and apply their spatial variability for proper irrigation scheduling. This study aimed to characterize the spatial variability and temporal stability of soil water contents at depths of 10 cm, 20 cm and 30 cm on flat (loamy soil) and hill-slope fields (silt-loamy soil). Field monitoring of soil moisture contents was used for variogram analysis using GS+ software. Kriging produced from the structural parameters of variogram was applied for the means of spatial prediction. The overall results showed that the surface soil moisture presented a strong spatial dependence at the sampling time and space in the field scale. The coefficient variation (CV) of soil moisture was within 7.0~31.3 % in a flat field and 8.3~39.4 % in a hill-slope field, which was noticeable in the dry season rather than the rainy season. The drought assessment analysis showed that only one day (Dec. 21st) was determined as dry (20.4 % and 24.5 % for flat and hill-slope fields, respectively). In contrary to a hill-slope field where the full irrigation was necessary, the centralized irrigation scheme was appeared to be more effective for a flat field based on the spatial variability of soil moisture contents. The findings of this study clearly showed that the geostatistical analysis of soil moisture contents greatly contributes to proper irrigation scheduling for water-efficient irrigation with maximal crop productivity and environmental benefits.

• 한국농공학회지
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• 제22권4호
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• pp.108-114
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• 1980

A deterministic conceptual erosion model which simulates detachment, entrainment, transport and deposition of eroded soil particles by rainfall impact and flowing water is presented. Both upland and channel phases of sediment yield are incorporated into the erosion model. The algorithms for the soil erosion and sedimentation processes including land and crop management effects are taken from the literature and then solved using a digital computer. The erosion model is used in conjunction with the modified Kentucky Watershed Model which simulates the hydrologic characteristics from watershed data. The two models are linked together by using the appropriate computer code. Calibrations for both the watershed and erosion model parameters are made by comparing the simulated results with actual field measurements in the Four Mile Creek watershed near Traer, Iowa using 1976 and 1977 water year data. Two water years, 1970 and 1978 are used as test years for model verification. There is good agreement between the mean daily simulated and recorded streamflow and between the simulated and recorded suspended sediment load except few partial differences. The following conclusions were drawn from the results after testing the watershed and erosion model. 1. The watershed and erosion model is a deterministic lumped parameter model, and is capable of simulating the daily mean streamflow and suspended sediment load within a 20 percent error, when the correct watershed and erosion parameters are supplied. 2. It is found that soil erosion is sensitive to errors in simulation of occurrence and intensity of precipitation and of overland flow. Therefore, representative precipitation data and a watershed model which provides an accurate simulation of soil moisture and resulting overland flow are essential for the accurate simulation of soil erosion and subsequent sediment transport prediction. 3. Erroneous prediction of snowmelt in terms of time and magnitute in conjunction with The frozen ground could be the reason for the poor simulation of streamflow as well as sediment yield in the snowmelt period. More elaborate and accurate snowmelt submodels will greatly improve accuracy. 4. Poor simulation results can be attributed to deficiencies in erosion model and to errors in the observed data such as the recorded daily streamflow and the sediment concentration. 5. Crop management and tillage operations are two major factors that have a great effect on soil erosion simulation. The erosion model attempts to evaluate the impact of crop management and tillage effects on sediment production. These effects on sediment yield appear to be somewhat equivalent to the effect of overland flow. 6. Application and testing of the watershed and erosion model on watersheds in a variety of regions with different soils and meteorological characteristics may be recommended to verify its general applicability and to detact the deficiencies of the model. Futhermore, by further modification and expansion with additional data, the watershed and erosion model developed through this study can be used as a planning tool for watershed management and for solving agricultural non-point pollution problems.

• 한국작물학회지
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• 제53권3호
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• pp.244-250
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• 2008

도정하지 않은 정조의 81 시료로부터 스펙트럼을 수집하고, 백미 완전미도정수율 예측 희귀모델을 개발하기 위해 검량식을 작성한 결과 스펙트럼을 8 nm 간격으로 지정하고, 1차미분 방법으로 검량식을 작성한 완전미율의 결정계수는 MPLS에서 0.8353, PLS 방법에서 0.8416, PCR에서 0.5277를 나타냈다. 스펙트럼을 20 nm 간격으로 지정하고 1차미분 방법으로 검량식을 작성하였다. 완전미율의 결정계수는 MPLS에서 0.8144, PLS 방법에서 0.8354, PCR에서 0.6809를 나타냈다. 스펙트럼을 8 nm 간격으로 지정하고 2차미분 방법으로 검량식을 작성하였다.완 전미율의 결정계수는 MPLS 방법에서 0.7994, PLS에서 0.8017, PCR에서 0.4473을 나타냈다. 스펙트럼을 20 nm 간격으로 지정하고 2차미분 방법으로 검량식을 작성하였다. 완전미율의 결정계수는MPLS 방법에서 0.8004, PLS에서 0.8493, PCR에서 0.6609을 나타냈다.

• 대한지리학회지
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• 제51권5호
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• pp.633-649
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• 2016

작물의 생산성은 생식기간 중 고온에 노출되면 감소한다. IPCC 5차 평가보고서는 고온의 빈도가 미래에 계속 증가할 것이며, 이는 세계 식량 공급에 영향을 미칠 것으로 전망하였다. 이 연구에서는 기상청의 Had GEM2-AO(the coupled atmosphere-ocean model of Hadley Centre Global Environmental Model version 2) 기후모델과 FAO/IIASA의 GAEZ(Global Agro-Ecological Zone) 작물모델 자료를 이용하여 전 지구 규모에서 4개의 주요 작물(쌀, 옥수수, 콩, 밀)에 대하여 기후변화로 인한 작물의 고온 스트레스를 평가하였다. 과거기간(1961~1990년)에 비해 미래(2070~2090년)에 생식기간 동안 최고기온은 약 $1.8{\sim}3.5^{\circ}C$ 상승할 것으로 전망되며, RCP2.6 시나리오에 비해 RCP8.5 시나리오에 따른 기온 상승이 더 클 것으로 전망된다. 특히 열 스트레스는 북반구 $30{\sim}50^{\circ}N$에 위치한 작물 생산 지역에 극심한 피해를 발생시킬 것으로 전망된다. RCP8.5 시나리오에 따르면 모든 작물에 대해서 전체 재배지역의 약 20%는 현재에 경험하지 못한 극단적인 고온 스트레스를 경험하게 될 것이며, 특히 북아메리카에서 쌀과 콩의 고온 스트레스 강도가 클 것으로 전망된다. 기후변화를 완화하기 위한 노력 없이 현재 추세대로 온실기체를 계속 배출한다면 온대 및 아열대 지역에서의 농업이 고온에 크게 영향을 받을 것으로 전망되며, 이는 작물의 대부분을 수입에 의존하는 우리나라 식량안보에 큰 위협이 될 수 있다. 그러므로 기후변화에 따른 식량안보에 대하여 지속적인 예측이 수행되어야 하며, 적응 전략 개발 및 적절한 농업 정책 등이 필요하다.

• 원예과학기술지
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• 제33권6호
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• pp.911-922
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• 2015

본 연구는 고랭지배추의 생산성에 관여하는 주요 요인을 분석하고, 실시간 계측한 생육 및 기상자료를 기반으로 고랭지배추의 수량을 예측하기 위한 모델을 개발하기 위하여 수행되었다. 먼저 수확 시의 전생육량변수에 의한 구중 추정식과 비파괴 측정 생육량 변수에 의한 추정식에 의한 설명력의 차이를 비교한 다음, 이를 보완하기 위하여 비파괴측정 생육량 변수에 생육도일(growing degree days, GDD)을 포함한 구중추정 회귀모형을 작성하고, 이 구중추정식을 GDD에 의한 엽생장량과 실측 생장량의 비, 토양수분에 따른 생육속도, 그리고 생장단계 및 기간에 따른 상대생장률을 적용하여 보정하였다. 비파괴 생육량과 GDD에 의한 구중 추정식은 y = 6897.5 - 3.57 ${\times}$ GDD - 136 ${\times}$ 엽폭 + 116 ${\times}$ 초고 + 155 ${\times}$ 구고 - 423 ${\times}$ 구폭 + 0.28 ${\times}$ 구고 ${\times}$ 구폭${\times}$ 구폭($r^2=0.989$)로 나타났다. 수량 산정을 위한 엑셀스프레드시트모형을 작성하였으며, 이 모형은 고랭지배추 실시간 생육data 시트, GDD 계산을 위한 일별 온도data 시트, 재배지 토양수분data 시트, 그리고 도출된 모형방정식에 병해충 및 재배관리에 의한 수시변동 변량과 보정값을 입력하여 단수를 산정하는 시트로 구성되어 있다. 작성된 엑셀스프레드시트 모형을 이용하여 재배면적 ${\times}$ 단위당 재식주수 ${\times}$ GDD와 비파괴 생육량에 근거한 예측구중 ${\times}$ GDD도입 보정값 ${\times}$ 토양수분 및 건조기간에 따른 보정값 ${\times}$ 상품률을 적용하여 권역별 수량을 산정하고 이들을 합산하여 고랭지배추의 총 생산량을 예측할 수 있을 것으로 판단되었다.

• Asian-Australasian Journal of Animal Sciences
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• 제8권3호
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• pp.281-287
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• 1995

A study was conducted to investigate the effect of different inclusion levels of urea treated whole-crop wheat silage (UWCWS) in grass silage based rations on the performance of growing beef cattle. The winter wheat (variety, Riband) was harvested (in the summer of 1991) at a dry matter proportion of 520 g/kg and treated with feed grade urea at the rate of 37 kg/tonne crop dry matter and preserved in a heavy duty plastic bag using a silo press. The urea treated whole crop wheat silage (UWCWS) was mixed with grass silage to replace 0.00 (S100), 0.33 (S33) and 0.67 (S67) parts of the forage dry matter and fed ad libitum in a cross over design to 18 Simmental X Holstein Friesian growing beef animals. Two energy sources {one high in starch, rolled barley (RB) and one high in digestible fibre, sugar beet pulp (SBP)} were fed to supply sufficient energy for the efficient use of nitrogen by the rumen micro-organisms. The data on DMIF (dry matter intake of forage), TDMI (total dry matter intake), DLWG (daily live weight gain), FCR (feed conversion ratio) were recorded and faecal samples were collected to determine the digestibility coefficients. Results revealed that with the inclusion of UWCW in the animals' diets the DMI of the forage was significantly increased (p < 0.05). The highest DMIF was found in the treatment "S33" ($6.28{\pm}0.25kg$) where 67% of the silage dry matter was replaced with the UWCW and the lowest value for DMIF was observed in the control treatment ($5.03{\pm}0.23kg$). The DLWG did not differ significantly between the treatments. However, treatment "S100" showed a trend towards a superior DLWG. Feed conversion ratio in the control treatment differed significantly from "S67" and "S33". The addition of the UWCW in the animals' diet resulted in the lower FCR There was no effect of type of energy supplement on any aspect of performance either overall or in interaction with grass silage: UWCWS ratio. The regression and correlation coefficients for DMIF (r = 5.22 + 0.0184x*), DLWG (r = $1.04-0.00086x^{NS}$) and FCR (r = 4.78 = 0.022x*) on the inclusion of UWCW in the diet were calculated. The effect of the inclusion of UWCW on the overall digestibility coefficients was significant (p < 0.05). The addition of the UWCWS in the diet decreased the digestibility of the DM, OM, ADF and NFE but effect on the protein digestibility was non significant. The results of present study suggests that a DLWG slightly over 1 kg can be achieved with UWCW during the store period (period in which animal performance targets are low especially during winter) and the prediction of ME was overestimated as the high intake of DM did not reflect in improved animal performance.

• 한국수자원학회논문집
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• 제56권12호
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• pp.883-893
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• 2023

2020년 기준 한국의 곡물자급률은 20.2%에 불과하지만 곡물수출국에서 발생하는 가뭄이 국내에 미치는 영향은 아직 면밀히 분석되지 않았다. 본 연구에서는 증발산 기반 가뭄지수인 Evaporative Stress Index (ESI)를 이용해 세계 주요 곡물생산지역의 농업가뭄의 발생빈도, 장기추세, 자연진동과의 상관성을 분석하였다. 또한 국제 곡물거래가격과 작물생산지역의 가뭄면적을 비교하여 해외에서 발생한 가뭄이 한국 경제에 미치는 영향을 정성적으로 평가하였다. ERA5 기후재분석자료로 산정된 ESI는 전지구적으로 토양수분과 강한 상관성을 보였으며 특히 작물재배 지역에서의 둘의 상관성이 매우 강하게 나타났다. 작물재배지역에서의 높은 상관성은 강한 지면-대기결합을 의미하며, 이 때문에 작은 토양수분 부족이 상대적으로 큰 수확량 손실로 연결될 가능성이 크다. 1991-2022 기간 작물재배지역에서 ESI는 뚜렷한 감소추세를 보였으며 지구온난화와 함께 가뭄면적이 증가할 가능성이 있다. 2012년과 2022년에 급격히 상승한 국제곡물가격은 수출국에서 발생한 대규모 가뭄과 밀접한 관계가 있는 것으로 분석되었으며 한국의 생산자물가지수를 상승시킨 주요 원인 중 하나로 판단된다. 본 연구는 해외지역에서 일어나는 가뭄의 영향을 줄이기 위해 감시와 위험관리 전략이 필요함을 시사한다.

• Journal of Plant Biotechnology
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• 제48권3호
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• pp.148-155
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• 2021

Gibberellins (GAs) are essential phytohormones for plant growth that influence developmental processes and crop yields. Recent functional genomic analyses of model plants have yielded good characterizations of the canonical GA signaling pathways and related genes. Although Panax ginseng has long been considered to have economic and medicinal importance, functional genomic studies of the GA signaling pathways in this crucial perennial herb plant have been rarely conducted. Here, we identified and performed functional analysis of the GA signaling-related genes, including PgGID1s, PgSLY1s, and PgRGAs. We confirmed that the physiological role of GA signaling components in P. ginseng was evolutionarily conserved. In addition, the important functional domains and amino acid residues for protein interactions among active GA, GID1, SCF^SLY1, and RGA were also functionally conserved. Prediction and comparison of crystallographic structural similarities between PgGID1s and AtGID1a supported their function as GA receptors. Moreover, the subcellular localization and GA-dependent promotion of DELLA degradation in P. ginseng was similar to the canonical GA signaling pathways in other plants. Finally, we found that overexpression of PgRGA2 and PgSLY1-1 was sufficient to complement the GA-related phenotypes of atgid1a/c double- and rga quintuple-mutants, respectively. This critical information for these GA signaling genes has the potential to facilitate future genetic engineering and breeding of P. ginseng for increased crop yield and production of useful substances.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.