• Title/Summary/Keyword: Agricultural systems

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Diversity, Interspecific Interaction and Abundance of Undergrowth in Monocultures and Integrated Systems of Natural Rubber Plantation in Danzhou, Southern China

  • Chima, Uzoma Darlington;Qi, Dongling;Wu, Zhixiang;Lan, Guoyu;Chen, Li
    • Journal of Forest and Environmental Science
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    • v.38 no.2
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    • pp.75-89
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    • 2022
  • The negative impact of monoculture rubber plantations on biodiversity and associated ecological processes/ecosystem services has led to suggestions on the use of integrated land use systems for rubber cultivation and production in order to ensure environmental sustainability. However, there is paucity of information on the effect of such integrated land use systems on the diversity and abundance of the rubber plantation undergrowth. We evaluated and compared undergrowth plant species composition, richness, abundance, diversity and interaction, in three integrated systems (Rubber-Strelitzia reginae Integrated System - RSrIS, Rubber-Podocarpus nagi Integrated System - RPnIS & Naturally Managed Rubber Plantation - NMRP) with three Rubber Monoculture Plantations (RMP1, RMP2 & RMP3) adjacent to the integrated systems, respectively, at the Investigation and Experiment Station of Tropical Crops, Danzhou, Hainan, China. Undergrowth species density was higher in the rubber monocultures than in the integrated systems except in RSrIS. Species richness and diversity were also higher in the monocultures except in NMRP. Species similarity/interaction between the monocultures and the integrated systems was highest between RMP3 and NMRP. The NRMP proved to be the best model of natural rubber integrated system for the conservation of undergrowth species richness, diversity and interspecific interaction. However, the conservation of undergrowth species in other forms of integrated natural systems can be enhanced by considering the ecology of species to be integrated in terms of their growth characteristics, competitive nature, and ability to grow in association with other species.

Glucose recovery from different corn stover fractions using dilute acid and alkaline pretreatment techniques

  • Aboagye, D.;Banadda, N.;Kambugu, R.;Seay, J.;Kiggundu, N.;Zziwa, A.;Kabenge, I.
    • Journal of Ecology and Environment
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    • v.41 no.7
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    • pp.191-201
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    • 2017
  • Background: Limited availability of corn stover due to the competing uses (organic manure, animal feed, bio-materials, and bioenergy) presents a major concern for its future in the bio-economy. Furthermore, biomass research has exhibited different results due to the differences in the supply of enzymes and dissimilar analytical methods. The effect of the two leading pretreatment techniques (dilute acid and alkaline) on glucose yield from three corn stover fractions (cob, stalk, and leaf) sourced from a single harvest in Uganda were studied at temperatures 100, 120, 140, and $160^{\circ}C$ over reaction times of 5, 10, 30, and 60 min. Results: From this study, the highest glucose concentrations obtained from the dilute acid (DA) pretreated cobs, stalks, and leaves were 18.4 g/L (66.8% glucose yield), 16.2 g/L (64.1% glucose yield), and 11.0 g/L (49.5% glucose yield), respectively. The optimal pretreatment settings needed to obtain these yields from the DA pretreated samples were at a temperature of $160^{\circ}C$ over an incubation time of 30 min. The highest glucose concentrations obtained from the alkaline (AL) pretreated cobs, stalks, and leaves were 24.7 g/L (81.73% glucose yield), 21.3 g/L (81.23% glucose yield), and 15.0 g/L (51.92% glucose yield), respectively. To be able to achieve these yields, the optimal pretreatment settings for the cobs and stalks were $140^{\circ}C$ and for a retention time of 30 min, while the leaves require optimal conditions of $140^{\circ}C$ and for a retention time of 60 min. Conclusions: The study recommends that the leaves could be left on the field during harvesting since the recovery of glucose from the pretreated cobs and stalks is higher.

Development of Methodology for Measuring Water Level in Agricultural Water Reservoir through Deep Learning anlaysis of CCTV Images (딥러닝 기법을 이용한 농업용저수지 CCTV 영상 기반의 수위계측 방법 개발)

  • Joo, Donghyuk;Lee, Sang-Hyun;Choi, Gyu-Hoon;Yoo, Seung-Hwan;Na, Ra;Kim, Hayoung;Oh, Chang-Jo;Yoon, Kwang-Sik
    • Journal of The Korean Society of Agricultural Engineers
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    • v.65 no.1
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    • pp.15-26
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    • 2023
  • This study aimed to evaluate the performance of water level classification from CCTV images in agricultural facilities such as reservoirs. Recently, the CCTV system, widely used for facility monitor or disaster detection, can automatically detect and identify people and objects from the images by developing new technologies such as a deep learning system. Accordingly, we applied the ResNet-50 deep learning system based on Convolutional Neural Network and analyzed the water level of the agricultural reservoir from CCTV images obtained from TOMS (Total Operation Management System) of the Korea Rural Community Corporation. As a result, the accuracy of water level detection was improved by excluding night and rainfall CCTV images and applying measures. For example, the error rate significantly decreased from 24.39 % to 1.43 % in the Bakseok reservoir. We believe that the utilization of CCTVs should be further improved when calculating the amount of water supply and establishing a supply plan according to the integrated water management policy.

Analysis of Inundation Area in the Agricultural Land under Climate Change through Coupled Modeling for Upstream and Downstream (상·하류 연계 모의를 통한 기후변화에 따른 농경지 침수면적 변화 분석)

  • Park, Seongjae;Kwak, Jihye;Kim, Jihye;Kim, Seokhyeon;Lee, Hyunji;Kim, Sinae;Kang, Moon Seong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.66 no.1
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    • pp.49-66
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    • 2024
  • Extreme rainfall will become intense due to climate change, increasing inundation risk to agricultural land. Hydrological and hydraulic simulations for the entire watershed were conducted to analyze the impact of climate change. Rainfall data was collected based on past weather observation and SSP (Shared Socio-economic Pathway)5-8.5 climate change scenarios. Simulation for flood volume, reservoir operation, river level, and inundation of agricultural land was conducted through K-HAS (KRC Hydraulics & Hydrology Analysis System) and HEC-RAS (Hydrologic Engineering Center - River Analysis System). Various scenarios were selected, encompassing different periods of rainfall data, including the observed period (1973-2022), near-term future (2021-2050), mid-term future (2051-2080), and long-term future (2081-2100), in addition to probabilistic precipitation events with return periods of 20 years and 100 years. The inundation area of the Aho-Buin district was visualized through GIS (Geographic Information System) based on the results of the flooding analysis. The probabilistic precipitation of climate change scenarios was calculated higher than that of past observations, which affected the increase in reservoir inflow, river level, inundation time, and inundation area. The inundation area and inundation time were higher in the 100-year frequency. Inundation risk was high in the order of long-term future, near-term future, mid-term future, and observed period. It was also shown that the Aho and Buin districts were vulnerable to inundation. These results are expected to be used as fundamental data for assessing the risk of flooding for agricultural land and downstream watersheds under climate change, guiding drainage improvement projects, and making flood risk maps.

State-of-The-Art Factory-Style Plant Production Systems

  • Takakura, Tadashi
    • Proceedings of the Korean Society for Bio-Environment Control Conference
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    • 1996.05a
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    • pp.1-10
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    • 1996
  • Factory-style plant production systems of various kinds are the final goal of greenhouse production systems. These systems facilitate planning for constant productivity per unit area and labor under various outside weather conditions, although energy consumption is intensive. Physical environmental control in combination with biological control can replace the use of agricultural chemicals such as insecticides, herbicides and hormones to regulate plants. In this way, closed systems which do not use such agricultural chemicals are ideal for environmental conservation for the future. Nutrient components in plants can be regulafied by physical environmental control including nutrient solution control in hydroponics. Therefore, specific contents of nutrients for particular plants can be listed on the container and be used as the basis of customer choice in the future. Plant production systems can be classified into three types based on the type of lighting: natural lighting, supplemental lighting and completely artificial lighting (Plant Factory). The amount of energy consumption increases in this order, although the degree of weather effects is in the reverse order. In the addition to lighting, factory-style plant production systems consist of mechanized and automated systems for transplanting, environmental control, hydroponics, transporting within the facility, and harvesting. Space farming and development of pharmaceutical in bio-reactors are other applications of these types of plant production systems. Various kinds of state-of-art factory-style plant production systems are discussed in the present paper. These systems are, in general, rather sophisticated and mechaized, and energy consumption is intensive. Factory-style plant production is the final goal of greenhouse production systems and the possibilities for the future are infinte but not clear.

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Artificial Neural Network-based Model for Predicting Moisture Content in Rice Using UAV Remote Sensing Data

  • Sarkar, Tapash Kumar;Ryu, Chan-Seok;Kang, Jeong-Gyun;Kang, Ye-Seong;Jun, Sae-Rom;Jang, Si-Hyeong;Park, Jun-Woo;Song, Hye-Young
    • Korean Journal of Remote Sensing
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    • v.34 no.4
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    • pp.611-624
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    • 2018
  • The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).

Analysis of components and applications of major crop models for nutrient management in agricultural land

  • Lee, Seul-Bi;Lim, Jung-Eun;Lee, Ye-Jin;Sung, Jwa-Kyung;Lee, Deog-Bae;Hong, Suk-Young
    • Korean Journal of Agricultural Science
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    • v.43 no.4
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    • pp.537-546
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    • 2016
  • The development of models for agriculture systems, especially for crop production, has supported the prediction of crop yields under various environmental change scenarios and the selection of better crop species or cultivar. Crop models could be used as tools for supporting reasonable nutrient management approaches for agricultural land. This paper outlines the simplified structure of main crop models (crop growth model, crop-soil model, and crop-soil-environment model) frequently used in agricultural systems and shows diverse application of their simulated results. Crop growth models such as LINTUL, SUCROS, could provide simulated data for daily growth, potential production, and photosynthesis assimilate partitioning to various organs with different physiological stages, and for evaluating crop nutrient demand. Crop-Soil models (DSSAT, APSIM, WOFOST, QUEFTS) simulate growth, development, and yields of crops; soil processes describing nutrient uptake from root zone; and soil nutrient supply capability, e.g., mineralization/decomposition of soil organic matter. The crop model built for the DSSAT family software has limitations in spatial variability due to its simulation mechanism based on a single homogeneous field unit. To introduce well-performing crop models, the potential applications for crop-soil-environment models such as DSSAT, APSIM, or even a newly designed model, should first be compared. The parameterization of various crops under different cultivation conditions like those of intensive farming systems common in Korea, shortened crop growth period, should be considered as well as various resource inputs.

DEVELOPMENT OF NIGHT COOLING SYSTEM FOR GREENHOUSE USING COOL AIR AND WATER FROM AN ABANDONED COAL MINE

  • Whoa S. Kang;Wie S. Kang;Lee, Gwi H.
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1996.06c
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    • pp.1136-1145
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    • 1996
  • This study was to develop the most effective cooling system which is needed to cool greenhouse during summer night to get early blooming of strawberries. Various cooling systems were designed and constructed to utilize the cool air and water from tan abandoned coal mine. Cooling systems built for this study were an evaporative cooling system with pad, cooling system using a small or large radiator , and duct cooling system using cool are drawn from coal mine. These systems were individual tested to investigate their effects on cooling greenhouse during summer night. Also, a combined cooling system was tested with operating an evaporative cooling system, small radiator, and duct cooling system simultaneously. The results in this study showed that individual cooling systems such as evaporative cooling system, small radiator, and cooling duct had about the same effect on cooling greenhouse. The combined system had little better cooling effect than that of individual cooling syst m except the large radiator . The most effective system for cooling of greenhouse was obtained with using a large a large radiator as the heat exchanger. With operating a large radiator, temperature inside the greenhouse was dropped to about 15-16$^{\circ}C$ while outside temperature was 23-24$^{\circ}C$ during summer night.

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