Relevant bibliographies by topics / Computer crop modeling

Academic literature on the topic 'Computer crop modeling'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Computer crop modeling"

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Kobayashi, Kent D. "COMPUTER SIMULATION PROGRAMS FOR TEACHING CROP MODELING." HortScience 27, no. 6 (June 1992): 671e—671. http://dx.doi.org/10.21273/hortsci.27.6.671e.

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The simulation programs Stella® (High Performance Systems) and Extend™ (Imagine That!) were used on Apple® Macintosh® computers in a graduate course on crop modeling to develop crop simulation models. Students developed models as part of their homework and laboratory assignments and their semester project Stella offered the advantage of building models using a relational diagram displaying state, rate, driving, and auxiliary variables. Arrows connecting the variables showed the relationships among the variables as information or material flows. Stella automatically kept track of differential equations and integration. No complicated programming was required of the students. Extend used the idea of blocks representing the different parts of a system. Lines connected the inputs and outputs to and from the different blocks. Extend was more flexible than Stella by giving the students the opportunity to do their own programming in a language similar to C. Also, with its dialog boxes, Extend more easily allowed the students to run multiple simulations answering “What if” questions. Both programs quickly enabled students to develop crop simulation models without the hindrance of extensive learning of a programming language or delving deeply into the mathematics of modeling.
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Heisler, M. G., and H. Jönsson. "Computer Modeling of Plant Development." Journal of Plant Growth Regulation 25, no. 4 (November 24, 2006): 267–69. http://dx.doi.org/10.1007/s00344-006-0080-z.

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Lin, Yuke, and Ying Zhang. "Research on Precision Cultivation of Digital Multimedia Crop Based on Predictive Computational Intelligence Technology." Advances in Multimedia 2021 (December 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/2864009.

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With the intersection and integration of modern crop cultivation and emerging disciplines, crop cultivation management is moving from traditional modeling and standardization to quantitative and intelligent direction. Crop precision cultivation technology is to apply system science and information technology to crop cultivation and digitally design information perception of the objects and processes involved in crop cultivation, dynamic simulation, so as to realize the quantification and accuracy of crop cultivation management. With the integration and intersection of modern digital multimedia crop cultivation and emerging disciplines, digital multimedia crop cultivation tends to gradually implement the quantitative and intelligent development, replacing the traditional scale and standardization. The technology of digital multimedia crop cultivation is to use science and information technology in digital multimedia crops to achieve the quantitative and precise characteristics of digital multimedia crop cultivation. The advancement of digital multimedia crop cultivation technology has greatly improved the management and benefits of the entire agricultural industry and has played a positive role in the development of agricultural information and modernity.
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Abdulhameed, Isam M., Sonay Sozudogru OK, Hala N. Malloki, Muhittin Onur Akca, Bilge Omar, and Gokhan Cayci. "Bio-Saline Agriculture Modeling, Using Saline Water for Irrigation Purposes." International Journal of Design & Nature and Ecodynamics 17, no. 6 (December 31, 2022): 951–56. http://dx.doi.org/10.18280/ijdne.170617.

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Climate change effects increase the scarcity of irrigation water and deterioration of its quality, which affects the crop water requirements. Researchers were studying the water recycling technique and finding about other possible renewable water resources for irrigation, they conclude that saline water can be used to meet part of the irrigation water needs for many crops under special field management, because there are many crops have a high tolerance to the salinity without decreasing in the yield. The current study aims to evaluate the economic yield of saline drainage water in irrigation. A computer program (Fıuat Ujaj) using Visual Basic language was constructed to use the largest possible amount of drainage water for irrigation after removing the toxic effects and then calculates the relative yield of the selected crops. The Main Outflow Drain (MOD) in Iraq was selected as a saline water resource which has 4.63 dS m-1 Electrical Conductivity (EC). This saline water contained high concentrations of chlorine and sodium ions. MFUP results showed that toxic effect of these ions can be removed by diluting with 35% of the nearby river water. MFUP results showed that the crops with high and medium tolerance to salinity give an acceptable yield ratio when they were irrigated with diluted water (35%) to remove toxicity effects only, while the dilution increases for crops of medium sensitivity, but the acceptable yield of sensitive crops is not achieved except with fresh river water. If the crop production is lower than the economically acceptable limit, another 5% of the river water will be added to mitigate, and the dilution process continues until the percentage of the river water become 80% of the irrigation water. If the required product is not achieved, then the program instructs to irrigate this crop with the river water only.
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Wang, Ze Fang, and Chen Liu. "Study on Greenhouse System Modeling Based on Adaptive Fuzzy Predictive Control." Applied Mechanics and Materials 602-605 (August 2014): 1237–39. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1237.

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In recent years, these governments are committed to intelligent greenhouse research, intelligent greenhouse system is a kind of resource conservation effective agricultural development technology, it is in the common greenhouse basis, combined with modern computer automatic control technology, intelligent sensing technology, artificial intelligence and expert system in high-tech field to develop, provide seasonal irrelevant for crop growth environment in a computer integrated control, to realize the various crops industrial production of high quality、 high efficient and low consumption[1]. With computer as the core of greenhouse comprehensive environment control system , get rapid development in Europe and the United States and Japan, then entered the network intelligent stage.Study of domestic greenhouse control system started relatively late, to the 80's, have the microcomputer control of artificial climate chamber , such as the Chongqing research institute MCU control system of the artificial climate chamber, as well as Shanghai the plant research institute artificial climate chamber[2].
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Ma, Xu, Tiejun Wang, and Lei Lu. "A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops." Remote Sensing 12, no. 8 (April 18, 2020): 1290. http://dx.doi.org/10.3390/rs12081290.

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In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.
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Kyryzyuk, Sergii, Vitaliy Krupin, Olena Borodina, and Adam Wąs. "Crop Residue Removal: Assessment of Future Bioenergy Generation Potential and Agro-Environmental Limitations Based on a Case Study of Ukraine." Energies 13, no. 20 (October 14, 2020): 5343. http://dx.doi.org/10.3390/en13205343.

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This study assesses the bioenergy generation potential of crop residues in Ukraine for the year 2030. Projections of agricultural development are made based on the Global Biosphere Management Model (GLOBIOM) and verified against available Agricultural Member State Modeling (AGMEMOD) results in regard to the six main crops cultivated in Ukraine (wheat, barley, corn, sunflower, rape and soya). Two agricultural development scenarios are assessed (traditional and innovative), facilitating the projection of future crop production volumes and yields for the selected crops. To improve precision in defining agro-environmental limitations (the share of crop residues necessary to be kept on the fields to maintain soil fertility for the continuous cultivation of crops), yield-dependent residue-to-product ratios (RPRs) were applied and the levels of available soil nutrients for regions of Ukraine (in regard to nitrogen, phosphorus, potassium and humus) were estimated. The results reveal the economically feasible future bioenergy generation potential of crop residues in Ukraine, equaling 3.6 Mtoe in the traditional agricultural development scenario and 10.7 Mtoe in the innovative development scenario. The projections show that, within the latter scenario, wheat, corn and barley combined are expected to provide up to 81.3% of the bioenergy generation potential of crop residues.
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Ma, Xu, and Yong Liu. "A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame." Remote Sensing 12, no. 21 (November 2, 2020): 3600. http://dx.doi.org/10.3390/rs12213600.

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The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops.
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Bekele, Bewket Getachew. "Review on Yield Gap Analysis: Modeling of Achievable Yields at Farm Level." European Journal of Agriculture and Forestry Research 10, no. 2 (February 15, 2022): 21–27. http://dx.doi.org/10.37745/ejafr.2013/vol9n22127.

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In the present context, ‘model’ is expressed as a computer program that can be repeatedly run several times for computing several designed mathematical or statistical expressions (equations) governing crop growth-environment relations, given appropriate input data. The experiment station yields obtained under a rainfed situation without any nutrient deficiency mostly considered as the potential yields of rainfed crops. Actual yields are obtained by recording crop yields of farmers in the region under investigation and achievable yield is between actual and potential yield. Actual yields are compared with the potential yields to estimate yield gaps of crops for that area and others which have the same agro-ecology. Achievable yield is determined by factors like availability of moisture and nutrients, Precipitation and irrigation as input, Soil profile water holding characteristics, Plant water balance (transpiration, water uptake), Soil water balance (evaporation, infiltration, runoff, flow, drainage) and Nitrogen fertilizer applications as input, Soil nitrogen conditions, Plant nitrogen balance (uptake, fixation, mobilization), Soil nitrogen balance (mineralization, immobilization, nitrification, denitrification). Generally, modeling Achievable yield of farm depend on water and nutrient data of the area and Actual yield is determined by factors like weeds, insect pests, diseases and pollutants.
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Pierre Pott, Luan, Telmo Jorge Carneiro Amado, Raí Augusto Schwalbert, Geomar Mateus Corassa, and Ignacio Antonio Ciampitti. "Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning." Computers and Electronics in Agriculture 201 (October 2022): 107320. http://dx.doi.org/10.1016/j.compag.2022.107320.

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Dissertations / Theses on the topic "Computer crop modeling"

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Song, Yu. "Modelling and analysis of plant image data for crop growth monitoring in horticulture." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/2032/.

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Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture.
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Mokoko-Mokeba, Michael Christian. "Computer simulation : modelling the dynamics of agrochemical sprays above and within a crop canopy." Thesis, University of Portsmouth, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310384.

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Shepherd, Anita. "Model to predict the effects of climate change on the yields of winter wheat." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309828.

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Georgis, Kidane. "The effect of fertiliser management practices on soil organic matter production in the semi-arid areas : a field and modelling approach." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09AFP/09afpg352.pdf.

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Bibliography: leaves 155-169. Studies the effect of nitrogen fertilizer on dry matter production under differing watering regimes. Investigates the accuracy of different crop and soil organic matter models for predicting crop yield, nitrogen uptake and changes in soil organic carbon and nitrogen. Compares the models with data from long-term field experiments on wheat in Australia and sorghum in Ethiopia. Finds that a higher crop yield and better nitrogen and water utilisation can be achieved if addition of nitrogen fertilizer is balanced with soil water.
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Harris, Robert H. "Identifying the cause of cereal yield decline in lucerne companion cropping systems; and the role of agronomy for mitigating cereal productivity losses." Thesis, 2011. http://hdl.handle.net/2440/70193.

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Integrating perennial plants like lucerne into farming systems has been widely recommended to mimic pre-agricultural native vegetation, to improve year round transpiration and reduce the off-site impacts of agriculture on the surrounding environment. Despite perennial plants providing greater hydrological benefits compared to traditional annual plant based farming systems; integration of lucerne into farming systems remains a challenge. One approach that may enhance the integration is companion cropping, where annual crops are sown directly into an existing lucerne stand. However, past research has shown that this practice can be harmful to the productivity of annual crops, due to competition with lucerne for environmental resources. Yet beyond quantifying the effect on annual crop production, little is understood about what causes the loss of yield. Under-standing the underlying mechanisms dictating the performance of annual crops growing with lucerne could help design agronomic strategies that mitigate competition, and improve annual crop productivity; in turn potentially improving industry acceptance and adoption of both lucerne and companion cropping. In this study, two field experiments showed that competition was apparent early in the growing season prior to cereal stem elongation; when cereal biomass in the presence of lucerne was significantly lower than that of cereal grown in monoculture. Although there were no differences in cereal establishment, companion cereals produced significantly (P<0.05) less tillers, spikes, cereal biomass, and consequently grain yield compared with cereals grown in monoculture. Both field experiments showed that fertiliser N could potentially increase companion cereal productivity, and that in-crop lucerne suppression could improve cereal grain quality by reducing lucerne pod contamination. Apart from quantifying the temporal effects of competition between the companion cereal and lucerne and assessing the role of agronomic strategies for mitigating competition, field experiments did not give much insight into what was causing the loss of companion cereal productivity. Simulation modelling using APSIM (Agricultural Production Systems Simulator) explored competition between the companion cereal and lucerne, and each component’s response to resource supply and agronomic intervention over longer periods. APSIM was found to satisfactorily simulate both simultaneous and stand alone wheat and lucerne growth, after comparison with field observed data. Although APSIM tended to deplete soil mineral N more rapidly under lucerne than field observations indicated, necessitating soil mineral N to be constrained within previously measured values in long-term simulations. Simulations showed that companion cereals were frequently sown into drier soil profiles, due to soil water extraction by lucerne over the preceding summer/autumn period, compared with monoculture cereals sown after the summer/autumn fallow. Competition for soil water appeared the major contributing factor to companion cereal performance, and simulated data predicted that companion cereals had to rely solely on in-crop rainfall. Therefore companion cropping in low rainfall environments where growing season rainfall (April to October) is less than 350 mm, or in environments where crops rely heavily on stored soil water at sowing for subsequent production, would be unsuitable for reliable grain production from companion cropping.
Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2011
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Books on the topic "Computer crop modeling"

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Cao, Weixing. Crop Modeling and Decision Support. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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White, Jeffrey W., and Peter Robert Grace. Modeling extremes of wheat and maize crop performance in the tropics: Proceedings of a workshop, CIMMYT, El Batán, México, 19-22 April 1999. [México, D.F., Mexico]: International Maize and Wheat Improvement Center, 2000.

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van, Laar H. H., ed. Modelling potential crop growth processes: Textbook with exercises. Dordrecht: Kluwer, 1993.

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van, Laar H. H., ed. Modelling potential crop growth processes: Textbook with exercises. Dordrecht: Kluwer Academic Publishers, 1994.

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Mokoko-Mokeba, Michael Christian Litombo. Computer simulation: modelling the dynamics of agrochemical sprays above and within a crop canopy. Portsmouth: University of Portsmouth, 1997.

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International, Potato Modeling Conference (2nd 1994 Wageningen Netherlands). Potato ecology and modelling of crops under conditions limiting growth. Dordrecht: Kluwer Academic Publishers, 1995.

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International Potato Modeling Conference (2nd 1994 Wageningen, Netherlands). Potato ecology and modelling of crops under conditions limiting growth. Dordrecht: Springer-Science+Business Media, 1995.

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Wei, Fang, ed. Climate under cover. 2nd ed. Dordrecht: Kluwer Academic Publishers, 2002.

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Climate under cover: Digital dynamic simulation in plant bio-engineering. Dordrecht: Kluwer Academic Publishers, 1993.

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Introduction to Mathematical Modeling of Crop Growth: How the Equations Are Derived And Assembled into a Computer Program. Brown Walker Press (FL), 2006.

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Book chapters on the topic "Computer crop modeling"

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Bi, Chunguang, and Guifen Chen. "Bayesian Networks Modeling for Crop Diseases." In Computer and Computing Technologies in Agriculture IV, 312–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18333-1_37.

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Barbier, Guillaume, Véronique Cucchi, and David R. C. Hill. "Contribution of Model-Driven Engineering to Crop Modeling." In Lecture Notes in Computer Science, 253–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39637-3_21.

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Zhou, Jiaogen, Xu Chen, Jingyin Zhao, and Dongsheng Wang. "Modeling and Simulating of Spatial Spread of Cross-Boundary Crop Diseases." In Computer and Computing Technologies in Agriculture VI, 101–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36124-1_13.

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Yang, ChuanHua, Ma Xu, ZhouFei Wang, WenWu Yang, and XingLong Liao. "Digital Modeling and Testing Research on Digging Mechanism of Deep Rootstalk Crops." In Computer and Computing Technologies in Agriculture III, 382–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12220-0_56.

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Kovalchuk, Pavlo, Viktoria Rozhko, Volodymyr Kovalchuk, Hanna Balykhina, and Olena Demchuk. "Crop Irrigation Demand Modelling Method Within the Conditions of Climate Change in the Dnipro-Donbas Canal." In Advances in Computer Science for Engineering and Education IV, 89–101. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80472-5_8.

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"COMPUTER IMPLEMENTATION." In Modeling Crop Production Systems, 225–329. CRC Press, 2008. http://dx.doi.org/10.1201/9781482280449-10.

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Ferreira, Virginia A., and William K. Lauenroth. "Computer Simulation Modeling of Pesticide Fate." In Pesticide Interactions in Crop Production, 87–111. CRC Press, 2018. http://dx.doi.org/10.1201/9781351075459-5.

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Postma, Johannes A., and Christopher K. Black. "Advances in root architectural modeling." In Understanding and improving crop root function, 3–32. Burleigh Dodds Science Publishing, 2021. http://dx.doi.org/10.19103/as.2020.0075.02.

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Root architectural (RSA) models have become important tools in root research and plant phenotyping for studying root traits, processes, and interactions with the environment. The models have been used to simulate how various root traits and processes influence water and nutrient uptake. At a more technical level, they have been used to develop phenotyping technology, particularly for testing algorithms for segmenting roots. To compute these quantitative estimates regarding plant nutrition and root functioning, much development occurred in the last decade increasing the complexity of the models. This chapter describes first the application of the models to questions in plant biology, breeding, and agronomy, and second the development of the models. It concludes with a small outlook suggesting that models need benchmarking and validation and that new developments are likely to include better descriptions of root plasticity responses and focus on biological interactions among (soil) organisms, including mycorrhizal fungi.
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Benabdelouahab, Tarik, Hayat Lionboui, Rachid Hadria, Riad Balaghi, Abdelghani Boudhar, and Bernard Tychon. "Support Irrigation Water Management of Cereals Using Optical Remote Sensing and Modeling in a Semi-Arid Region." In Geospatial Technologies for Effective Land Governance, 124–45. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5939-9.ch008.

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Irrigated agriculture is an important strategic sector for Morocco, contributing to food security and employment. Nowadays, irrigation scheme managers shall ensure that water is optimally used. The main objective was to support the irrigation monitoring and management of wheat in the irrigated perimeter using optical remote sensing and crop modeling. The potential of spectral indices derived from SPOT-5 images was explored for quantifying and mapping surface water content changes at large scale. Indices were computed using the reflectance in red, near infrared, and shortwave infrared bands. A field crop model (AquaCrop) was adjusted and tested to simulate the grain yield and the temporal evolution of soil moisture status. This research aimed at providing a scientific and technical approach to assist policymakers and stakeholders to improve monitoring irrigation and mitigating wheat water stress at field and irrigation perimeter levels in semi-arid areas. The approach could lead to operational management tools for an efficient irrigation at field and regional levels.
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Dharumarajan S., Veeramani S., Kalaiselvi Beeman, Lalitha M., Janani N., Srinivasan R., and Rajendra Hegde. "Potential Impacts of Climate Change on Land Degradation and Desertification." In Advances in Environmental Engineering and Green Technologies, 183–95. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7387-6.ch010.

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Land degradation and desertification have been graded as a major environmental and social dispute in most of the emerging countries. Changes in temperature, wind speed, and precipitation patterns will influence plant biomass production, land use, land cover, soil moisture, infiltration rate, runoff and crop management, and ultimately, land degradation. Close relations between climate change and land degradation processes have been perceived in the past decades. Climate change models and land use models should be combined with hydrologic/erosion models to accurately compute or predict climate change impacts on land degradation. This chapter introduces the advancements in modeling of impact of climate changes in land degradation and need for the critical investigation to better understand and forecast the responses of land degradation processes to a changing climate in the future.
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Conference papers on the topic "Computer crop modeling"

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Stupen, Roman, Nazar Stupen, Zoriana Ryzhok, Oksana Stupen, and Halyna Dudych. "Crop Yielding Capacity Modeling using Artificial Neural Networks." In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2021. http://dx.doi.org/10.1109/csit52700.2021.9648681.

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Qi Yuan, Xianglin Cheng, and Peng He. "Modeling of virtual crop development based on multi-agent." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622297.

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Radulescu, Marius, Gheorghita Zbaganu, and Constanta Zoie Radulescu. "Crop Planning in the Presence of Production Quotas (Invited Paper)." In Tenth International Conference on Computer Modeling and Simulation (uksim 2008). IEEE, 2008. http://dx.doi.org/10.1109/uksim.2008.40.

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Guoquan Jiang and Cuijun Zhao. "A vision system based crop rows for agricultural mobile robot." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5623244.

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Deng, Lujuan, and Huaishan Wang. "Fuzzy logic technology for modeling of greenhouse crop transpiration rate." In Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, edited by Jiancheng Fang and Zhongyu Wang. SPIE, 2006. http://dx.doi.org/10.1117/12.718289.

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Si, Hai-ping, Wei Fang, Peng Tang, and Yong-sheng Cao. "Efficient implementation of data integration and sharing of crop germplasm resources investigation." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620152.

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Xiu Lina, Liu Xiangnan, and Huang Lingxiang. "Evaluation model of crop contamination stress level based on dynamic fuzzy neural network." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5619425.

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Ojo, Mike O., Davide Adami, and Stefano Giordano. "Network Performance Evaluation of a LoRa-based IoT System for Crop Protection Against Ungulates." In 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2020. http://dx.doi.org/10.1109/camad50429.2020.9209317.

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Ciappi, Raquel, Bradley Miller, Stephen Petfield, Christopher Uthgenannt, James Glancey, Kevin Smith, and Melanie Harkcom. "Design of a Protective Curtain System for High Rotational Speed Agricultural Disc Mowers." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87570.

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A new protective curtain system for agricultural disc mowers has been designed and validated through a combination of novel testing methods. In order to understand performance requirements and choose the best solution to the problem, computer modeling, as well as small and full scale prototype testing was conducted. The new curtain system improves upon the current production design by adding a flexible joint between the top of the shroud and the curtain. This results in a reduction of force exerted on the crops by the curtain, the key performance metric for this project. A full scale prototype (P1) was constructed in order to confirm concept functionality, which led to a second generation prototype (P2) made of a heavier weight curtain fabric, in order to meet safety metrics. This new mounting method will improve cutting quality by reducing the force exerted on the crop by the curtain, while still meeting the safety requirements, in addition to providing a superior, more marketable product for Case-New Holland and higher crop yield for the customer.
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Hu, Lei, Xiao-Dong Bai, Aiping Yang, Kun Zhang, Chonghua Zhang, and Bo Liu. "Crop extraction based on ultra-simple neural network modeling in the normalized rgb and CIE L*a*b* color spaces." In MIPPR 2019: Pattern Recognition and Computer Vision, edited by Zhenbing Liu, Jayaram K. Udupa, Nong Sang, and Yuehuan Wang. SPIE, 2020. http://dx.doi.org/10.1117/12.2541856.

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Reports on the topic "Computer crop modeling"

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Dudley, Lynn M., Uri Shani, and Moshe Shenker. Modeling Plant Response to Deficit Irrigation with Saline Water: Separating the Effects of Water and Salt Stress in the Root Uptake Function. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586468.bard.

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Standard salinity management theory, derived from blending thermodynamic and semi- empirical considerations leads to an erroneous perception regarding compensative interaction among salinity stress factors. The current approach treats matric and osmotic components of soil water potential separately and then combines their effects to compute overall response. With deficit water a severe yield decrease is expected under high salinity, yet little or no reduction is predicted for excess irrigation, irrespective of salinity level. Similarly, considerations of competition between chloride and nitrate ions have lead to compensation hypothesis and to application of excess nitrate under saline conditions. The premise of compensative interaction of growth factors behind present practices (that an increase in water application alleviates salinity stress) may result in collateral environmental damage. Over-irrigation resulting in salinization and elevated ground water threatens productivity on a global scale. Other repercussions include excessive application of nitrate to compensate for salinity, unwillingness to practice deficit irrigation with saline water, and under-utilization of marginal water. The objectives for the project were as follows: 1) To develop a database for model parameterization and validation by studying yield and transpiration response to water availability, excessive salinity and salt composition. 2) To modify the root sink terms of an existing mechanism-based model(s) of water flow, transpiration, crop yield, salt transport, and salt chemistry. 3) To develop conceptual and quantitative models of ion uptake that considers the soil solution concentration and composition. 4) To develop a conceptual and quantitative models of effects of NaCl and boron accumulation on yield and transpiration. 5) To add a user interface to the water flow, transpiration, crop yield, salt transport, chemistry model to make it easy for others to use. We conducted experiments in field plots and lysimeters to study biomass production and transpiration of com (Zeamays cv. Jubilee), melon (Cucumismelo subsp. melo cv. Galia), tomato (Lycopersiconesculentum Mill. cv. 5656), onion (Alliumcepa L. cv. HA 944), and date palms (Phoenix Dactylifera L. cv. Medjool) under salinity combined with water or with nitrate (growth promoters) or with boron (growth inhibitor). All factors ranged from levels not limiting to plant function to severe inhibition. For cases of combined salinity with water stress, or excess boron, we observed neither additive nor compensative effects on plant yield and transpiration. In fact, yield and transpiration at each combination of the various factors were primarily controlled by one of them, the most limiting factor to plant activity. We proposed a crop production model of the form Yr = min{gi(xi), where Yr = Yi ym-1 is relative yield,Ym is the maximum yield obtained in each experiment, Xi is an environmental factor, gi is a piecewise-linear response function, Yi is yield of a particular treatment. We selected a piecewise-linear approach because it highlights the irrigation level where the response to one factor ceases and a second factor begins. The production functions generate response "envelopes" containing possible yields with diagonal lines represent response to Xi alone and the lines parallel to the X-axis represent response to salinity alone. A multiplicative model was also derived approximating the limiting behaviour for incorporation in a hydrochemical model. The multiplicative model was selected because the response function was required to be continuous. The hydrochemical model was a better predictor of field-measured water content and salt profiles than models based on an additive and compensative model of crop response to salinity and water stress.
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