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Journal articles on the topic 'Crop parameter'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Wijesingha, Jayan, Supriya Dayananda, Michael Wachendorf, and Thomas Astor. "Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters." Sensors 21, no. 8 (April 20, 2021): 2886. http://dx.doi.org/10.3390/s21082886.

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Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2

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Wallach, Daniel, Bruno Goffinet, Jacques-Eric Bergez, Philippe Debaeke, Delphine Leenhardt, and Jean-Noël Aubertot. "Parameter Estimation for Crop Models." Agronomy Journal 93, no. 4 (July 2001): 757–66. http://dx.doi.org/10.2134/agronj2001.934757x.

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3

Stanghellini, C., and W. Th M. van Meurs. "CROP TRANSPIRATION: A GREENHOUSE CLIMATE CONTROL PARAMETER." Acta Horticulturae, no. 245 (August 1989): 384–88. http://dx.doi.org/10.17660/actahortic.1989.245.51.

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4

Jacobs, Adrie F. G., and John H. Van Boxel. "Computational parameter estimation for a maize crop." Boundary-Layer Meteorology 42, no. 3 (February 1988): 265–79. http://dx.doi.org/10.1007/bf00123816.

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Tremblay, Marie, and Daniel Wallach. "Comparison of parameter estimation methods for crop models." Agronomie 24, no. 6-7 (September 2004): 351–65. http://dx.doi.org/10.1051/agro:2004033.

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T. Zhai, R. H. Mohtar, F. El-Awar, W. Jabre, and J. J. Volenec. "PARAMETER ESTIMATION FOR PROCESS-ORIENTED CROP GROWTH MODELS." Transactions of the ASAE 47, no. 6 (2004): 2109–19. http://dx.doi.org/10.13031/2013.17796.

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Manoharan, Dr Samuel. "Supervised Learning for Microclimatic parameter Estimation in a Greenhouse environment for productive Agronomics." September 2020 2, no. 3 (July 17, 2020): 170–76. http://dx.doi.org/10.36548/jaicn.2020.3.004.

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Maximum crop returns are essential in modern agriculture due to various challenges caused by water, climatic conditions, pests and so on. These production uncertainties are to be overcome by appropriate evaluation of microclimate parameters at commercial scale for cultivation of crops in a closed-field and emission free environment. Internet of Things (IoT) based sensors are used for learning the parameters of the closed environment. These parameters are further analyzed using supervised learning algorithms under MATLAB Simulink environment. Three greenhouse crop production systems as well as

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Zhao, Xia, Xingchuan Wang, Guangchao Cao, Kelong Chen, Wenjia Tang, and Zhijun Zhang. "Crop Identification by Using Seasonal Parameters Extracted from Time Series Landsat Images in a Mountainous Agricultural County of Eastern Qinghai Province, China." Journal of Agricultural Science 9, no. 4 (March 14, 2017): 116. http://dx.doi.org/10.5539/jas.v9n4p116.

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Time series vegetable indexes (Vis) have been evidenced a useful data to extract vegetable phenology and identify crop types. This paper conducted such a research in Qinghai Province by using Landsat TM images, via four steps, i) sampling single-crop plots and extracting crop spectrums based on pure signle-crop pixels; ii) building time-series vegetable indexes by using Landsat 8 TM images (2013-2014); iii) extracting seasonal parameters according to algorithms defined in TIMESAT program; vi) generating a decision tree for identifying crop types and validate classification accuracy via ground

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Bahrami, Hazhir, Saeid Homayouni, Abdolreza Safari, Sayeh Mirzaei, Masoud Mahdianpari, and Omid Reisi-Gahrouei. "Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations." Agronomy 11, no. 7 (July 3, 2021): 1363. http://dx.doi.org/10.3390/agronomy11071363.

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Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correl

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Zeng, Wenzhi, Yuchao Lu, Amit Kumar Srivastava, Thomas Gaiser, and Jiesheng Huang. "Parameter Sensitivity and Uncertainty of Radiation Interception Models for Intercropping System." Ecological Chemistry and Engineering S 27, no. 3 (September 1, 2020): 437–56. http://dx.doi.org/10.2478/eces-2020-0028.

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AbstractEstimating the interception of radiation is the first and crucial step for the prediction of production for intercropping systems. Determining the relative importance of radiation interception models to the specific outputs could assist in developing suitable model structures, which fit to the theory of light interception and promote model improvements. Assuming an intercropping system with a taller and a shorter crop, a variance-based global sensitivity analysis (EFAST) was applied to three radiation interception models (M1, M2 and M3). The sensitivity indices including main (Si) and

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Wallach, Daniel, Bruno Goffinet, and Marie Tremblay. "Parameter estimation in crop models: exploring the possibility of estimating linear combinations of parameters." Agronomie 22, no. 2 (March 2002): 171–78. http://dx.doi.org/10.1051/agro:2002004.

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Kremer, Cristian, Carlos Faúndez, Víctor Beyá-Marshall, Nicolas Franck, and Víctor Muñoz-Aravena. "Transpiration-use efficiency of young cactus pear plants (Opuntia ficus-indica L.)." International Journal of Agriculture and Natural Resources 48, no. 2 (2021): 115–24. http://dx.doi.org/10.7764/ijanr.v48i2.2255.

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Opuntia ficus-indica is a versatile crop that is resilient to drought, making it perfect for semiarid to arid zones. However, the lack of knowledge associated with its benefits and the lack of simple crop growth simulation models to determine its potential development, among others, has prevented its expansion. Transpiration-use efficiency (w) has been used to evaluate crop performance under different water supplies; however, the lack of consistency in w values under different environmental conditions has impeded its use as a transferable parameter. To overcome this problem, w is estimated thr

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Nantasaksiri, Kotchakarn, Patcharawat Charoen-Amornkitt, and Takashi Machimura. "Land Potential Assessment of Napier Grass Plantation for Power Generation in Thailand Using SWAT Model. Model Validation and Parameter Calibration." Energies 14, no. 5 (March 1, 2021): 1326. http://dx.doi.org/10.3390/en14051326.

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In Thailand, Napier grass is expected to play an important role as an energy resource for future power generation. To accomplish this goal, numerous areas are required for Napier grass plantations. Before introducing crops, the land potential of the country and the impact of crops on the environment should be assessed. The soil and water assessment tool (SWAT) model is very useful in investigating crop impacts and land potential. Unfortunately, the crop growth parameters of Napier grass are yet to be identified and, thus, conducting effective analysis has not been possible. Accordingly, in thi

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Reisi Gahrouei, O., S. Homayouni, and A. Safari. "ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 885–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-885-2019.

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Abstract. The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop characterization. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in the management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which

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CLOUTIS, E. A., D. R. CONNERY, D. J. MAJOR, and F. J. DOVER. "Airborne multi-spectral monitoring of agricultural crop status: effect of time of year, crop type and crop condition parameter." International Journal of Remote Sensing 17, no. 13 (September 1996): 2579–601. http://dx.doi.org/10.1080/01431169608949094.

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16

de Souza, Romina, M. Teresa Peña-Fleitas, Rodney B. Thompson, Marisa Gallardo, and Francisco M. Padilla. "Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper." Remote Sensing 12, no. 5 (February 26, 2020): 763. http://dx.doi.org/10.3390/rs12050763.

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Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each

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17

McCullagh, Peter, and David Clifford. "Evidence for conformal invariance of crop yields." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2071 (February 28, 2006): 2119–43. http://dx.doi.org/10.1098/rspa.2006.1667.

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The aim of this paper is to study the nature of spatial correlation of yields of agricultural crops. The focus is primarily on natural or non-anthropogenic spatial variation, patterns that cannot be explained by topography, by variety or treatment effects, or by agricultural practices. Conformal invariance implies stationarity and isotropy, and also determines the rate of decay of spatial correlations. The resulting Gaussian model is studied empirically to see whether it describes satisfactorily the pattern of spatial correlations observed in field trials of various crops. By embedding the law

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18

Tomíček, Jiří, Jan Mišurec, and Petr Lukeš. "Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations." Remote Sensing 13, no. 18 (September 13, 2021): 3659. http://dx.doi.org/10.3390/rs13183659.

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In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PR

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19

Acharya, Subodh, Melanie Correll, James W. Jones, Kenneth J. Boote, Phillip D. Alderman, Zhengjun Hu, and C. Eduardo Vallejos. "Reliability of Genotype-Specific Parameter Estimation for Crop Models: Insights from a Markov Chain Monte-Carlo Estimation Approach." Transactions of the ASABE 60, no. 5 (2017): 1699–712. http://dx.doi.org/10.13031/trans.12183.

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Abstract. Parameter estimation is a critical step in successful application of dynamic crop models to simulate crop growth and yield under various climatic and management scenarios. Although inverse modeling parameterization techniques significantly improve the predictive capabilities of models, whether these approaches can recover the true parameter values of a specific genotype or cultivar is seldom investigated. In this study, we applied a Markov Chain Monte-Carlo (MCMC) method to the DSSAT dry bean model to estimate (recover) the genotype-specific parameters (GSPs) of 150 synthetic recombi

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20

Singha, Chiranjit, Kishore Chandra Swain, and Sanjay Kumar Swain. "Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability." Agriculture 10, no. 6 (June 9, 2020): 213. http://dx.doi.org/10.3390/agriculture10060213.

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Crop selections and rotations are very important in optimising land and labour productivities, enhancing higher cropping intensities, producing better crop yield. A land suitability analysis system based on the analytical hierarchy process (AHP) technique coupled with the Geographic Information System (GIS) software environment can be a unique tool for better crop selection. The AHP-GIS technique was used in land suitability analysis in crop rotation decisions, for rice-jute (Kharif season) and potato-lentil (Rabi season) crops in the Hooghly District, West Bengal, India. The study area coveri

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21

JOLLIFFE, PETER A., and FREDRICK M. WANJAU. "Competition and productivity in crop mixtures: some properties of productive intercrops." Journal of Agricultural Science 132, no. 4 (June 1999): 425–35. http://dx.doi.org/10.1017/s0021859699006450.

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Inverse yield–density relationships were used to evaluate how competitive balances in mixed plant species associations may influence productivity, as measured by three indices : Relative Yield Total (RYT), Relative Land Output (RLO), and Total Land Output (TLO). Parameters of the inverse relationships, which express competitive influences and plant growth potential in the absence of competition, were incorporated into expressions used to predict RYT, RLO and TLO. Initial parameter values were derived from 25 experiments on binary species mixtures, and parameter values were systematically varie

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22

Heuer, A. I. "Studying Parameter Sensitivity and Behaviour of the Crop Model STICS." Open Hydrology Journal 5, no. 1 (May 30, 2011): 58–68. http://dx.doi.org/10.2174/1874378101105010058.

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23

Rillig, Matthias C., and Anika Lehmann. "Exploring the agricultural parameter space for crop yield and sustainability." New Phytologist 223, no. 2 (March 7, 2019): 517–19. http://dx.doi.org/10.1111/nph.15744.

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24

VIVEKANAND, VIVEKANAND, VINOD KUMAR, VIJAY KUMAR SINGH, and BHASKAR PRATAP SINGH. "Weather parameter based crop planning in Tarai region of Uttarakhand." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 10, no. 2 (October 15, 2017): 360–66. http://dx.doi.org/10.15740/has/ijae/10.2/360-366.

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25

Fang, Quanxiao, L. Ma, R. D. Harmel, Q. Yu, M. W. Sima, P. N. S. Bartling, R. W. Malone, B. T. Nolan, and J. Doherty. "Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm." Agronomy 9, no. 5 (May 10, 2019): 241. http://dx.doi.org/10.3390/agronomy9050241.

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An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to ex

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Sun, Jun, Guo Qing Zhang, Zhuang Chen, Lei Gao, and Jun Ming Li. "Cultivation Management and Standard Library Automatic Query System of Digital Greenhouse." Applied Mechanics and Materials 190-191 (July 2012): 64–69. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.64.

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The research and applied technology about agriculture are attracting increasing attention, and especially digital greenhouses have become an important part of efficient agriculture. In this paper, basing on the environmental factors and crops growth status information, management system sorts out, analyzes and summarizes the data to give the best environment parameters for different crops in different external environment and to give the best growth parameter value for crop in different growth stages. A standard database of digital greenhouse will be built in this paper. Basing on the database

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27

Challinor, A. J., T. R. Wheeler, J. M. Slingo, and D. Hemming. "Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO 2 climates." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2085–94. http://dx.doi.org/10.1098/rstb.2005.1740.

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The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO 2 ) climates by perturbation of parameters in each model. The climate sensitivity parameter ( λ , the equilibrium response of global mean surfa

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Bilionis, I., B. A. Drewniak, and E. M. Constantinescu. "Crop physiology calibration in the CLM." Geoscientific Model Development 8, no. 4 (April 15, 2015): 1071–83. http://dx.doi.org/10.5194/gmd-8-1071-2015.

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Abstract. Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given

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Bilionis, I., B. A. Drewniak, and E. M. Constantinescu. "Crop physiology calibration in CLM." Geoscientific Model Development Discussions 7, no. 5 (October 14, 2014): 6733–71. http://dx.doi.org/10.5194/gmdd-7-6733-2014.

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Abstract. Farming is using more terrestrial ground, as population increases and agriculture is increasingly used for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmo

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30

Yang, Chenyao, Christoph Menz, Helder Fraga, Samuel Reis, Nelson Machado, Aureliano C. Malheiro, and João A. Santos. "Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method." Agronomy 11, no. 8 (August 20, 2021): 1659. http://dx.doi.org/10.3390/agronomy11081659.

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Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Do

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31

P. Chothani, Ekta, H. J. Kapadiya, M. F. Acharya, and C. M. Bhaliya. "Impact of Weather Parameter on Early Blight Epidemiology in Tomato Crop." International Journal of Current Microbiology and Applied Sciences 6, no. 11 (November 10, 2017): 3160–66. http://dx.doi.org/10.20546/ijcmas.2017.611.370.

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32

P. N. Singh, J. P. Mitchell, and W. W. Wallender. "Parameter Optimization for Predicting Soil Water Movement under Crop Residue Cover." Transactions of the ASABE 54, no. 6 (2011): 2029–35. http://dx.doi.org/10.13031/2013.40661.

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Garcia, A., and R. G. B. André. "ANALYSIS OF THE PRIESTLEY-TAYLOR ALPHA PARAMETER FOR A BEAN CROP." Acta Horticulturae, no. 537 (October 2000): 151–57. http://dx.doi.org/10.17660/actahortic.2000.537.15.

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34

Moeckel, Thomas, Supriya Dayananda, Rama Nidamanuri, Sunil Nautiyal, Nagaraju Hanumaiah, Andreas Buerkert, and Michael Wachendorf. "Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images." Remote Sensing 10, no. 5 (May 22, 2018): 805. http://dx.doi.org/10.3390/rs10050805.

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KUMAR, DEWENDRA, R. K. NAIK, P. K. NISHAD, and P. R. SAHU. "Optimization of crop-machine parameter on the performance of Kodo pearler." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 10, no. 2 (October 15, 2017): 545–49. http://dx.doi.org/10.15740/has/ijae/10.2/545-549.

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Dai, Chunni, Meng Yao, Zhujie Xie, Chunhong Chen, and Jingao Liu. "Parameter optimization for growth model of greenhouse crop using genetic algorithms." Applied Soft Computing 9, no. 1 (January 2009): 13–19. http://dx.doi.org/10.1016/j.asoc.2008.02.002.

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Djaby, Bakary, Allard De Wit, Louis Kouadio, Moussa El Jarroudi, and Bernard Tychon. "Spatial Distribution of Calibrated WOFOST Parameters and Their Influence on the Performances of a Regional Yield orecasting System." Sustainable Agriculture Research 2, no. 4 (July 15, 2013): 12. http://dx.doi.org/10.5539/sar.v2n4p12.

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We investigate in this study (i) a redefinition of crop variety zonations at a spatial scale of 10x10 km, and (ii) the influence of recalibrated crop parameters on regional yield forecasting of winter wheat and grain maize in western Europe. The baseline zonation and initial crop parameter set was derived from the operational European crop growth monitoring system (CGMS) which involves the agrometeorological model WOFOST. Air temperature data from 325 weather stations over the 1992-2007 period were used to define new zonations in a 300 x 300 km test site. Two parameters which influenced mostly

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Rezaei, M., P. Seuntjens, I. Joris, W. Boënne, S. Van Hoey, P. Campling, and W. M. Cornelis. "Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters." Hydrology and Earth System Sciences 20, no. 1 (January 29, 2016): 487–503. http://dx.doi.org/10.5194/hess-20-487-2016.

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Abstract. Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis

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Wan, Xingyu, Qingxi Liao, Yajun Jiang, and Yitao Liao. "Cattle Feeding Experiment and Chopping Device Parameter Determination for Mechanized Harvesting of Forage Rape Crop." Transactions of the ASABE 64, no. 2 (2021): 715–25. http://dx.doi.org/10.13031/trans.14341.

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HighlightsForage rape crop could effectively alleviate the lack of green forage for livestock in winter.With the growth of forage rape crop, stem lignification was exacerbated and its palatability degenerated.The relationship between particle length and palatability was explored in a cattle feeding experiment.Optimal working parameters of the chopping device were obtained for harvesting the crop in different stages.Abstract. Forage rape crop, which uses the immature plant leaf and stem of a hybrid rape crop (Brassica napus L.) with low erucic acid and glucosinolate to feed livestock, is an inn

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Cousens, R. "An empirical model relating crop yield to weed and crop density and a statistical comparison with other models." Journal of Agricultural Science 105, no. 3 (December 1985): 513–21. http://dx.doi.org/10.1017/s0021859600059396.

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SUMMARYA hyperbolic model relating crop yield to weed density is extended to include crop density as a further variable. Other models were obtained from published sources, eight being originally applied to yield of above-ground biomass and six to marketable yield. Data were obtained from a field experiment in which spring wheat and spring barley were planted either in monoculture or together and at a range of densities. Further data were obtained from a published experiment on Sinapis alba and barley grown in containers. The models were fitted to data using maximum likelihood estimation. Compa

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Rezaei, M., P. Seuntjens, I. Joris, W. Boënne, S. Van Hoey, P. Campling, and W. M. Cornelis. "Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters." Hydrology and Earth System Sciences Discussions 12, no. 7 (July 20, 2015): 6881–920. http://dx.doi.org/10.5194/hessd-12-6881-2015.

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Abstract. Monitoring and modeling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil-water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed. Free drainage and incremental constant head conditions was implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis showe

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Young, Bryan G., David J. Gibson, Karla L. Gage, Joseph L. Matthews, David L. Jordan, Micheal D. K. Owen, David R. Shaw, Stephen C. Weller, and Robert G. Wilson. "Agricultural Weeds in Glyphosate-Resistant Cropping Systems in the United States." Weed Science 61, no. 1 (March 2013): 85–97. http://dx.doi.org/10.1614/ws-d-12-00001.1.

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A segment of the debate surrounding the commercialization of genetically engineered (GE) crops, such as glyphosate-resistant (GR) crops, focuses on the theory that implementation of these traits is an extension of the intensification of agriculture that will further erode the biodiversity of agricultural landscapes. A large field-scale study was conducted in 2006 in the United States on 156 different field sites with a minimum 3-yr history of GR corn, cotton, or soybean in the cropping system. The impact of cropping system, crop rotation, frequency of using the GR crop trait, and several categ

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Tumusiime, Emmanuel, Brorsen B. Wade, Jagadeesh Mosali, Jim Johnson, James Locke, and Jon T. Biermacher. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models." Journal of Agricultural and Applied Economics 43, no. 4 (November 2011): 541–52. http://dx.doi.org/10.1017/s1074070800000067.

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The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nonstochastic or “limited” stochastic. In this study, we estimate rye–ryegrass yield functions in which all parameters are random. The three functional forms considered are the linear response plateau, the quadratic, and the Spillman-Mitscherlich. Nonstochastic yield models are rejected in favor of stochastic parameter models. Quadratic functional forms fit the data poorly. Optimal nitrogen application recommendations are calculated for the linear response plateau and Spillman-Mitscherl

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Nasirzadehdizaji, Rouhollah, Fusun Balik Sanli, Saygin Abdikan, Ziyadin Cakir, Aliihsan Sekertekin, and Mustafa Ustuner. "Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage." Applied Sciences 9, no. 4 (February 15, 2019): 655. http://dx.doi.org/10.3390/app9040655.

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The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R2) of 0.82 and 0.81 indicate that there is a strong relationship between the maize he

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Pook, Torsten, Manfred Mayer, Johannes Geibel, Steffen Weigend, David Cavero, Chris C. Schoen, and Henner Simianer. "Improving Imputation Quality in BEAGLE for Crop and Livestock Data." G3: Genes|Genomes|Genetics 10, no. 1 (November 1, 2019): 177–88. http://dx.doi.org/10.1534/g3.119.400798.

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Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 ou

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Shastry, K. Aditya, H. A. Sanjay, and Abhijeeth Deshmukh. "A Parameter Based Customized Artificial Neural Network Model for Crop Yield Prediction." Journal of Artificial Intelligence 9, no. 1-3 (December 15, 2015): 23–32. http://dx.doi.org/10.3923/jai.2016.23.32.

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Hu, Jian Ming, Xiao He Guo, and Guang Hui Li. "Crop Growth Environment Parameter Measurement and Control System Based on ARM Framework." Applied Mechanics and Materials 734 (February 2015): 242–46. http://dx.doi.org/10.4028/www.scientific.net/amm.734.242.

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Agricultural environment monitoring is the basic function of intelligent greenhouses, it broke through the geographical, environmental, and climate conditions on the influence of the crops, it is of great significance to the agricultural modernization and intelligentization, and promotes the development of agricultural advancement and intelligentization. Agricultural environment monitoring system based on ARM framework,PC S3C6410 is used as main control chip, running under WinCE environment, a good human-computer interface is provided; C8051F120 microcontroller as the core of its lowercompute,

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HU, S., and X. MO. "Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods." Journal of Agricultural Science 152, no. 1 (November 27, 2012): 119–33. http://dx.doi.org/10.1017/s0021859612000901.

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SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then use

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Zhao, Shuailing, and Zhibin Zhang. "A new recognition of crop row based on its structural parameter model." IFAC-PapersOnLine 49, no. 16 (2016): 431–38. http://dx.doi.org/10.1016/j.ifacol.2016.10.079.

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César Trejo Zúñiga, Elmer, Irineo Lorenzo López Cruz, and Agustín Ruíz García. "Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms." Applied Soft Computing 23 (October 2014): 474–82. http://dx.doi.org/10.1016/j.asoc.2014.06.023.

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