• 한국작물학회:학술대회논문집
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• 한국작물학회 1986년도 춘계 학술대회지
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• pp.30-31
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• 1986

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.126-126
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• 2022

Climate change has continued to impact meteorological factors like rainfall in many countries including Nigeria. Thus, altering the rainfall patterns which subsequently affect the crop yield. Maize is an important cereal grown in northern Nigeria, along with sorghum, rice, and millet. Due to the challenge of water scarcity during the dry season, it has become critical to design appropriate strategies for planning, developing, and management of the limited available water resources to increase the maize yield. This study, therefore, determines the quantity of water required to produce maize from planting to harvesting and the impact of drought on maize during different growth stages in the region. Rainfall data from six rain gauge stations for a period of 36 years (1979-2014) was considered for the analysis. The standardized precipitation and evapotranspiration index (SPEI) is used to evaluate the severity of drought. Using the CROPWAT model, the evapotranspiration was calculated using the Penman-Monteith method, while the crop water requirements (CWRs) and irrigation scheduling for the maize crop was also determined. Irrigation was considered for 100% of critical soil moisture loss. At different phases of maize crop growth, the model predicted daily and monthly crop water requirements. The crop water requirement was found to be 319.0 mm and the irrigation requirement was 15.5 mm. The CROPWAT 8.0 model adequately estimated the yield reduction caused by water stress and climatic impacts, which makes this model appropriate for determining the crop water requirements, irrigation planning, and management.

• Journal of Biosystems Engineering
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• 제23권3호
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• pp.285-290
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• 1998

Growth information of crops is essential for efficient control of greenhouse environment. However, a few non-invasive and continuous monitoring methods of crop growth has been developed. A computer vision system with a CCD camera and a frame grabber was developed to conduct non-destructive and intact plant growth analyses. The developed system was evaluated by conducting the growth analysis of lettuce. A linear model that explains the relationship between the relative crop coverage by the crop canopy and dry weight of a lettuce was presented. It was shown that this measurement method could estimate the dry weight from the relative crop coverage by the crop canopy. The result also showed that there was a high correlation between the projected top leaf area and the dry weight of the lettuce.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2011년도 학술발표회
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• pp.16-18
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• 2011

Evaluation of climate change on the rice productivity was conducted using crop growth simulation model, where Odae, Hwaseong, Ilpum were used as a representative cultivar of early, medium, and medium-late rice maturity type, respectively, and climate change scenario 'A1B' was applied to weather data for future climate change at 57sites. When cropping season was fixed, rice yield decreased by 4~35% as climate change which was caused by poor filled grain ratio with high temperature and low irradiation during grain-filling. When cropping season was changed, rice yield decreased by only 0~5% as climate change which was caused poor filled grain ratio with low irradiation during grain-filling period. However, this irradiation decline was less than when cropping season was fixed. Therefore, we need to develop rice cultivars resistant to low irradiation which can maintain high filled grain ratio under poor irradiation condition, and late maturity rice cultivars whose growing period is longer than the present medium-late maturity type.

• 한국환경농학회지
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• 제39권3호
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• pp.204-213
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• 2020

BACKGROUND: Since the number of crops cultivated in reclaimed land is huge, it is very difficult to quantify the total crop production. Therefore, a non-destructive method for predicting crop production is needed. Salt tolerant root vegetables such as red beets and sugar beet are suitable for cultivation in reclaimed land. If their underground biomass can be predicted, it helps to estimate crop productivity. Objectives of this study are to investigate maximum leaf length and weight of red beet, sugar beet, and turnips grown in reclaimed land, and to determine optimal model with regression analysis for linear and allometric growth models. METHODS AND RESULTS: Maximum leaf length, width, and root fresh weight of red beets, sugar beets, and turnips were measured. Ten linear models and six allometric growth models were selected for estimation of root fresh weight and non-linear regression analysis was conducted. The allometric growth model, which have a variable multiplied by square of maximum leaf length and maximum leaf width, showed highest R² values of 0.67, 0.70, and 0.49 for red beets, sugar beets, and turnips, respectively. Validation results of the models for red beets and sugar beets showed the R² values of 0.63 and 0.65, respectively. However, the model for turnips showed the R² value of 0.48. The allometric growth model was suitable for estimating the root fresh weight of red beets and sugar beets, but the accuracy for turnips was relatively low. CONCLUSION: The regression models established in this study may be useful to estimate the total production of root vegetables cultivated in reclaimed land, and it will be used as a non-destructive method for prediction of crop information.

• 한국작물학회:학술대회논문집
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• 한국작물학회 1998년도 춘계 학술대회지
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• pp.101-102
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• 1998

• 한국유기농업학회지
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• 제25권2호
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• 한국농림기상학회지
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• 제22권2호
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• pp.68-78
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• 2020

생산자뿐만 아니라 소비자에게 상당한 경제적인 영향을 줄 수 있는 채소 작황 정보를 사전에 예측하기 위해 작물 모형들이 사용될 수 있다. 채소의 생육과 수확량을 추정하기 위한 모형들은 대다수 작물에 대해 개발되어 있지 못하며 이는 고품질의 생육 관측 자료들이 축적되지 않았기 때문이다. 본 연구에서는 배추, 무, 마늘, 양파 및 고추의 5대 채소들을 대상으로 작물 모형 개발과 검증을 위한 생육 자료를 수집할 때 사용되는 프로토콜을 분석하고 이를 개선하고자 하였다. 작물 모형의 모수추정을 위해 사용되는 관측 프로토콜은 통계청과 농촌진흥청 프로토콜들의 단점을 보완하는 방식으로 개선될 수 있다. 작물모형은 기상조건에 따른 작물의 생육 반응을 예측하기 위해 사용되기 때문에 신뢰도 높은 기상 관측 자료를 확보할 수 있는 지역에서 표본 필지를 선정하는 것이 유리할 것이다. 또한, 최소한의 표본 조사 필지에서 상세한 관측자료 수집하기 위해 관심 작물이 재배되고 있는 지역 중에서 기후 특성이 상이한 지점들을 대상으로 표본 조사 필지들을 선정하는 것이 권장된다. 작물 생육 모형의 개발 및 검증을 위해서는 시계열적으로 얻어지는 작물 생육 모의값과 비교하기 위해 일정 시간 간격별로 관측 자료를 수집하는 것이 필수적이며, 기존의 프로토콜에 제시되지 않았던 생육 초기의 관측값을 확보하는 방향으로 개선되어야 할 것이다. 병해충 조사항목들과 기상재해 양상과 관련한 항목들이 작물모형 개발을 위한 관측 프로토콜에 포함된다면, 작물모형과 병해충 모형을 개발하고 이들 모형들을 통합하는 방식으로 실제 수량과 가까운 작황예측이 가능할 것이다. 또한, 표본조사 필지에서 다수의 구역을 설정하고, 이로부터 샘플을 채취하는 것이 관측자료의 신뢰도를 높일 수있다. 본 연구에서 제안된 프로토콜을 사용하여 얻어진 관측자료들이 자료 공유 플랫폼을 통해 제공된다면 채소 작물의 작황 예측을 위한 작물 모형 개발이 활성화될 것이다.

• 스마트미디어저널
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• 제11권2호
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• pp.53-62
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• 2022

산업화 이후 가속화된 지구 온난화 현상으로 인해 기존환경 변화 및 이상기후 발생 빈도가 증가하고 있다. 농업은 기후변화에 매우 민감한 분야의 산업으로 지구 온난화는 작물의 생산량을 감소시키고 재배 지역이 변하는 등의 문제를 발생시킨다. 또한, 환경 변화는 작물의 생육 시기를 불규칙하게 만들어 숙련된 농사꾼들도 작물의 생육단계를 쉽게 추정할 수 없도록 만들어 여러 문제를 발생시킨다. 이에 본 논문에서는 작물의 생육단계를 추정하기 위한 CNN(Convolution Neural Network) 모델을 제안한다. 제안한 모델은 ResNet의 Pooling Layer를 수정한 모델로 ResNet, DenseNet 모델의 생육단계 추정보다 높은 성능 결과를 확인하였다.

• 한국유기농업학회지
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• 제25권4호
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• pp.805-819
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• 2017

The study was carried out to develop a mathematical model for evaluating the effect of organic fertilizers in organic rice production systems. A function to simulate the nitrogen mineralization process in the paddy soil has been developed and integrated into ORYZA2000 crop growth model. Inorganic nitrogen in the soil was estimated by single exponential models, given temperature and C:N ratio of organic amendments. Data collected from the two-year field experiment were used to evaluate the performance of the model. The revised version of ORYZA2000 provided reasonable estimates of key variables for nitrogen dynamics and crop growth in the organic rice production systems. Coefficient of determination between the measured value and simulated value were 0.6613, 0.8938, and 0.8092, respectively for soil inorganic nitrogen, total dry matter production, and rice yield. This means that the model could be used to quantify nitrogen supplying capacity of organic fertilizers relative to chemical fertilizer. Nitrogen dynamics and rice growth simulated by the model would be useful information to make decision for organic fertilization in organic rice production systems.