Relevant bibliographies by topics / Crop parameter

Academic literature on the topic 'Crop parameter'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Crop parameter"

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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 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.
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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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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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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 the outdoor environment are analyzed for comparison and model-based evaluation of the microclimate parameters using the IoT sensors. This analysis prior to cultivation enables creating better environment and thus increase the productivity and harvest. The supervised learning algorithm offers self-tuning reference inputs based on the crop selected. This offers a flexible architecture and easy analysis and modeling of the crop growth stages. On comparison of three greenhouse environment as well as outdoor settings, the functional reliability as well as accuracy of the sensors are tested for performance and validated. Solar radiation, vapor pressure deficit, relative humidity, temperature and soil fertility are the raw data processed by this model. Based on this estimation, the plant growth stages are analyzed by the comfort ratio. The different growth stages, light conditions and time frames are considered for determining the reference borders for categorizing the variation in each parameter. The microclimate parameters can be assessed dynamically with comfort ratio index as the indicator when multiple greenhouses are considered. The crop growth environment is interpreted better with the Simulink model and IoT sensor nodes. The result of supervised learning leads to improved efficiency in crop production developing optimal control strategies in the greenhouse environment.
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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 investigation. The results indicate that crops planted in a larger continuous range, such as spring wheat, potato and rapeseed, achieved an acceptable accuracy of above 70%, while crops planted too dispersedly (like broad bean, which is often inter-planted with other crops) or with a too smaller planting range (like barley), remained a poor recognition rates (below 50%). The value of this work lies in it displayed not only the classification accuracy of crop types in this region by using such methodology, but also the feasibility of integrating VIs calculation, seasonal parameter extracting and decision tree generation into one computer program, which is highly desired in this region.
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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 correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.
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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 total effects (STi) of the fraction of intercepted radiation by the taller (ftaller), the shorter (fshorter) and both intercrops together (fall) were quantified with different perturbations of the geometric arrangement of the crops (10-60 %). We found both ftaller and fshorter in M1 are most sensitive to the leaf area index of the taller crop (LAItaller). In M2, based on the main effects, the leaf area index of the shorter crop (LAIshorter) replaces LAItaller and becomes the most sensitive parameter for fshorter when the perturbations of widths of taller and shorter crops (Wtaller and Wshorter) become 40 % and larger. Furthermore, in M3, ftaller is most sensitive to LAItaller while fshorter is most sensitive to LAIshorter before the perturbations of geometry parameters becoming larger than 50 %. Meanwhile, LAItaller, LAIshorter, and Ktaller are the three most sensitive parameters for fall in all three models. From the results we conclude that M3 is the most plausible radiation interception model among the three models.
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Dissertations / Theses on the topic "Crop parameter"

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Perkins, Seth A. "Crop model review and sweet sorghum crop model parameter development." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14037.

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Master of Science
Department of Biological and Agricultural Engineering
Kyle Douglas-Mankin
Opportunities for alternative biofuel feedstocks are widespread for a number of reasons: increased environmental and economic concerns over corn production and processing, limitations in the use of corn-based ethanol to 57 billion L (15 billion gal) by the Energy Independence and Security Act (US Congress, 2007), and target requirements of 136 billion L (36 billion gal) of renewable fuel production by 2022. The objective of this study was to select the most promising among currently available crop models that have the potential to model sweet sorghum biomass production in the central US, specifically Kansas, Oklahoma, and Texas, and to develop and test sweet sorghum crop parameters for this model. Five crop models were selected (CropSyst, CERE-Sorghum, APSIM, ALMANAC, and SORKAM), and the models were compared based on ease of use, model support, and availability of inputs and outputs from sweet sorghum biomass data and literature. After reviewing the five models, ALMANAC was selected as the best suited for the development and testing of sweet sorghum crop parameters. The results of the model comparison show that more data are needed about sweet sorghum physiological development stages and specific growth/development factors before the other models reviewed in this study can be readily used for sweet sorghum crop modeling. This study used a unique method to calibrate the sweet sorghum crop parameter development site. Ten years of crop performance data (Corn and Grain Sorghum) for Kansas Counties (Riley and Ellis) were used to select an optimum soil water (SW) estimation method (Saxton and Rawls, Ritchie et al., and a method that added 0.01 m m [superscript]-1 to the minimum SW value given in the SSURGO soil database) and evapotranspiration (ET) method (Penman-Montieth, Priestley-Taylor, and Hargraeves and Samani) combination for use in the sweet sorghum parameter development. ALMANAC general parameters for corn and grain sorghum were used for the calibration/selection of the SW/ET combination. Variations in the harvest indexes were used to simulate variations in geo-climate region grain yield. A step through comparison method was utilized to select the appropriate SW/ET combination. Once the SW/ET combination was selected the combination was used to develop the sweet sorghum crop parameters. Two main conclusions can be drawn from the sweet sorghum crop parameter development study. First, the combination of Saxton and Rawls (2006) and Priestley-Taylor (1972) (SR-PT) methods has the potential for wide applicability in the US Central Plains for simulating grain yields using ALMANAC. Secondly, from the development of the sweet sorghum crop model parameters, ALMANAC modeled biomass yields with reasonable accuracy; differences from observed biomass values ranged from 0.89 to 1.76 Mg ha [superscript]-1 (2.8 to 9.8%) in Kansas (Riley County), Oklahoma (Texas County), and Texas (Hale County). Future research for sweet sorghum physiology, Radiation Use Efficiency/Vapor Pressure Deficit relationships, and weather data integration would be useful in improving sweet sorghum biomass modeling.
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Lamsal, Abhishes. "Crop model parameter estimation and sensitivity analysis for large scale data using supercomputers." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/34576.

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Doctor of Philosophy
Department of Agronomy
Stephen M. Welch
Global crop production must be doubled by 2050 to feed 9 billion people. Novel crop improvement methods and management strategies are the sine qua non for achieving this goal. This requires reliable quantitative methods for predicting the behavior of crop cultivars in novel, time-varying environments. In the last century, two different mathematical prediction approaches emerged (1) quantitative genetics (QG) and (2) ecophysiological crop modeling (ECM). These methods are completely disjoint in terms of both their mathematics and their strengths and weaknesses. However, in the period from 1996 to 2006 a method for melding them emerged to support breeding programs. The method involves two steps: (1) exploiting ECM’s to describe the intricate, dynamic and environmentally responsive biological mechanisms determining crop growth and development on daily/hourly time scales; (2) using QG to link genetic markers to the values of ECM constants (called genotype-specific parameters, GSP’s) that encode the responses of different varieties to the environment. This can require huge amounts of computation because ECM’s have many GSP’s as well as site-specific properties (SSP’s, e.g. soil water holding capacity). Moreover, one cannot employ QG methods, unless the GSP’s from hundreds to thousands of lines are known. Thus, the overall objective of this study is to identify better ways to reduce the computational burden without minimizing ECM predictability. The study has three parts: (1) using the extended Fourier Amplitude Sensitivity Test (eFAST) to globally identify parameters of the CERES-Sorghum model that require accurate estimation under wet and dry environments; (2) developing a novel estimation method (Holographic Genetic Algorithm, HGA) applicable to both GSP and SSP estimation and testing it with the CROPGRO-Soybean model using 182 soybean lines planted in 352 site-years (7,426 yield observations); and (3) examining the behavior under estimation of the anthesis data prediction component of the CERES-Maize model. The latter study used 5,266 maize Nested Associated Mapping lines and a total 49,491 anthesis date observations from 11 plantings. Three major problems were discovered that challenge the ability to link QG and ECM’s: 1) model expressibility, 2) parameter equifinality, and 3) parameter instability. Poor expressibility is the structural inability of a model to accurately predict an observation. It can only be solved by model changes. Parameter equifinality occurs when multiple parameter values produce equivalent model predictions. This can be solved by using eFAST as a guide to reduce the numbers of interacting parameters and by collecting additional data types. When parameters are unstable, it is impossible to know what values to use in environments other than those used in calibration. All of the methods that will have to be applied to solve these problems will expand the amount of data used with ECM’s. This will require better optimization methods to estimate model parameters efficiently. The HGA developed in this study will be a good foundation to build on. Thus, future research should be directed towards solving these issues to enable ECM’s to be used as tools to support breeders, farmers, and researchers addressing global food security issues.
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Coppa, Isabel Patricia Maria, and Isabel coppa@csw com au. "The use of remote sensing data for broad acre grain crop monitoring in Southeast Australia." RMIT University. Mathematical and Geospatial Sciences, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070201.095831.

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In 2025, there will be almost 8 billion people to feed as the worlds population rapidly increases. To meet domestic and export demands, Australian grain productivity needs to approximately triple in the next 20 years, and this production needs to occur in an environmentally sustainable manner. The advent of Hi-tech Precision Farming in Australia has shown promise in recent time to optimize the use of resources. Most
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Abebe, Yibekal Alemayehu. "Managing the soil water balance of hot pepper (Capsicum annuum L.) to improve water productivity." Thesis, University of Pretoria, 2010. http://hdl.handle.net/2263/25257.

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A series of field, rainshelter, growth cabinet and modelling studies were conducted to investigate hot pepper response to different irrigation regimes and row spacings; to generate crop-specific model parameters; and to calibrate and validate the Soil Water Balance (SWB) model. Soil, climate and management data of five hot pepper growing regions of Ethiopia were identified to develop irrigation calendars and estimate water requirements of hot pepper under different growing conditions. High irrigation regimes increased fresh and dry fruit yield, fruit number, harvest index and top dry matter production. Yield loss could be prevented by irrigating at 20-25% depletion of plant available water, confirming the sensitivity of the crop to mild soil water stress. High plant density markedly increased fresh and dry fruit yield, water-use efficiency and dry matter production. Average fruit mass, succulence and specific leaf area were neither affected by row spacing nor by irrigation regimes. There were marked differences among the cultivars in fruit yields despite comparable top dry mass production. Average dry fruit mass, fruit number per plant and succulence were significantly affected by cultivar differences. The absence of interaction effects among cultivar and irrigation regimes, cultivars and row spacing, and irrigation regimes and row spacing for most parameters suggest that appropriate irrigation regimes and row spacing that maximize productivity of hot pepper can be devised across cultivars. To facilitate irrigation scheduling, a simple canopy cover based procedure was used to determine FAO-type crop factors and growth periods for different growth stages of five hot pepper cultivars. Growth analysis was done to calculate crop-specific model parameters for the SWB model and the model was successfully calibrated and validated for five hot pepper cultivars under different irrigation regimes or row spacings. FAO basal crop coefficients (Kcb) and crop-specific model parameters for new hot pepper cultivars can now be estimated from the database, using canopy characteristics, day degrees to maturity and dry matter production. Growth cabinet studies were used to determine cardinal temperatures, namely the base, optimum and cut-off temperatures for various developmental stages. Hot pepper cultivars were observed to require different cardinal temperatures for various developmental stages. Data on thermal time requirement for flowering and maturity between plants in growth cabinet and open field experiments matched closely. Simulated water requirements for hot pepper cultivar Mareko Fana production ranged between 517 mm at Melkassa and 775 mm at Alemaya. The simulated irrigation interval ranged between 9 days at Alemaya and 6 days at Bako, and the average irrigation amount per irrigation ranged between 27.9 mm at Bako and 35.0 mm at Zeway.
Thesis (PhD)--University of Pretoria, 2010.
Plant Production and Soil Science
unrestricted
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Rabe, Nicole J., and University of Lethbridge Faculty of Arts and Science. "Remote sensing of crop biophysical parameters for site-specific agriculture." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2003, 2003. http://hdl.handle.net/10133/195.

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Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture Analysis (SMA), a relatively new sub-pixel scale image processing method that derives the fraction of sunlit canopy, soil and shadow that is contributing to a pixel's relectance. SMA of three crop types (peas, wheat and canola) performed slightly better than conventional vegetation indices in predicting leaf area index (LAI) and biomass using Probe-1 imagery acquired early in the growing season. Other potential advantages for SMA were also indentified, and it was conclude that future research is warranted to assess the full potential of SMA in a multi-temporal sense throughout the growing season.
xiv, 194 leaves : ill. (some col.) ; 29 cm.
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Jalali-Farahani, Hamid Reza 1960. "Crop water stress parameters for turfgrass and their environmental dependability." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/191950.

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The concept of crop water stress index (CWSI) was explored using empirical and theoretical models to evaluate bermudagrass water status. The empirical methods were simplifications of the crop energy balance equation. Measured field data were employed to develop the empirical CWSI parameters. Field data were collected from turf plots under three levels of irrigation for the 1986 growing season in Tucson, Arizona. The simplest empirical model of Idso gave the highest variance in estimates of CWSI for all treatments with the estimates being highly influenced by net radiation. An improved empirical model was developed when net radiation was included in the statistical analysis of the canopy temperature minus air temperature limits. In general, the most accurate estimates of CWSI were obtained by using the energy balance equation with constant values of potential canopy and aerodynamic resistances. Various methods were used to evaluate these resistances. Further research is needed to test the perfomance of the theoretically-derived CWSI and to develop more general methods of evaluating the resistances.
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Burkart, Andreas [Verfasser]. "Multitemporal assessment of crop parameters using multisensorial flying platforms / Andreas Burkart." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1096330075/34.

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Varella, Hubert Vincent. "Inversion d’un modèle de culture pour estimer spatialement les propriétés des sols et améliorer la prédiction de variables agro-environnementales." Thesis, Avignon, 2009. http://www.theses.fr/2009AVIG0638/document.

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Les modèles de culture constituent des outils indispensables pour comprendre l’influence des conditions agropédoclimatiques sur le système sol-plante à différentes échelles spatiales et temporelles. A l’échelle locale de la parcelle agricole, le modèle peut être utilisé dans le cadre de l’agriculture de précision pour optimiser les pratiques de fertilisation azotée de façon à maximiser le rendement ou le revenu tout en minimisant le lessivage des nitrates vers la nappe. Cependant, la pertinence de l’utilisation du modèle repose sur la qualité des prédictions réalisées, basée entre autres sur une bonne détermination des paramètres d’entrée du modèle. Dans le cadre de l’agriculture de précision, les paramètres concernant les propriétés des sols sont les plus délicates à connaître en tout point de la parcelle et il existe très peu de cartes de sols permettant de les déterminer de manière précise. Néanmoins, dans ce contexte, on peut disposer d’observations acquises automatiquement sur l’état du système sol-plante, telles que des images de télédétection, les cartes de rendement ou les mesures de résistivité électrique du sol. Il existe alors une alternative intéressante pour estimer les propriétés des sols à l’échelle de la parcelle qui consiste à inverser le modèle de culture à partir de ces observations pour retrouver les valeurs des propriétés des sols. L’objectif de cette thèse consiste (i) dans un premier temps à analyser les performances d’estimation des propriétés des sols par inversion du modèle STICS à partir de différents jeux d’observations sur des cultures de blé et de betterave sucrière, en mettant en oeuvre une méthode bayésienne de type Importance Sampling, (ii) dans un second temps à mesurer l’amélioration des prédictions de variables agro-environnementales réalisées par le modèle à partir des valeurs estimées des paramètres. Nous montrons que l’analyse de sensibilité globale permet de quantifier la quantité d’information contenue dans les jeux d’observations et les performances réalisées en matière d’estimation des paramètres. Ce sont les propriétés liées au fonctionnement hydrique du sol (humidité à la capacité au champ, profondeur de sol, conditions initiales) qui bénéficient globalement de la meilleure performance d’estimation par inversion. La performance d’estimation, évaluée par comparaison avec l’estimation fournie par l’information a priori, dépend fortement du jeu d’observation et est significativement améliorée lorsque les observations sont faites sur une culture de betterave, les conditions climatiques sont sèches ou la profondeur de sol est faible. Les prédictions agro-environnementales, notamment la quantité et la qualité du rendement, peuvent être grandement améliorées lorsque les propriétés du sol sont estimées par inversion, car les variables prédites par le modèle sont également sensibles aux propriétés liées à l’état hydrique du sol. Pour finir, nous montrons dans un travail exploratoire que la prise en compte d’une information sur la structure spatiale des propriétés du sol fournie par les mesures de résistivité électrique, peut permettre d’améliorer l’estimation spatialisée des propriétés du sol. Les observations acquises automatiquement sur le couvert végétal et la résistivité électrique du sol se révèlent être pertinentes pour estimer les propriétés du sol par inversion du modèle et améliorer les prédictions des variables agro-environnementales sur lesquelles reposent les règles de choix des pratiques agricoles
Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters,especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless,observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In my work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, I started my study with a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, I chose a Bayesian data assimilation method (because I have prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prio rinformation and the result with the posterior information provided by the Bayesian data assimilation method. The result of the parameter estimation show that the whole set of parameter has a better quality of estimation when observations on sugar beet are assimilated. At the same time, global sensitivity analysis of the observed variables to the 7 soil parameters have been performed, allowing me to build a criterion based on sensitivity indices (provided by the global sensitivity analysis) able to rank the parameters with respect to their quality of estimate. This criterion constitutes an interesting tool for determining which parameters it is possible to estimate to reduce probably the uncertainties on the predictions. The prediction of the crop behaviour when estimating the soil parameters is then studied. Indeed, the quality of prediction of agro-environmental variables of the STICS crop model (yield, protein of the grain and nitrogen balance at harvest) is determined by comparing the result of the prediction using the prior information on the parameters and the result using the posterior information. As for the estimation of soil parameters, the prediction of the variable is made on different climatic and agronomic conditions. According to the result of parameter estimation, assimilating observations on sugar beet lead to a better quality ofprediction of the variables than observations on wheat. It was also shown that the number ofcrop seasons observed and the number of observations improve the quality of the prediction
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Maqrot, Sara. "Méthodes d'optimisation combinatoire en programmation mathématique : Application à la conception des systèmes de verger-maraîcher." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30131.

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Dans le cadre du développement durable et des innovations dans les systèmes agroalimentaires, les systèmes mixtes horticoles (vergers et maraîchage) visent à répondre aux enjeux actuels auxquels l'agriculture est confrontée, à savoir une diminution de la pollution des sols, une meilleure gestion des ressources (eau, énergies) et un enrichissement de la biodiversité, tout en continuant d'assurer des fonctions alimentaires. Ils combinent des productions à la fois diversifiées et relativement intensifiées, leur permettant de s'insérer en périphérie urbaine. Ces systèmes agroforestiers reposent sur un ensemble complexe d'interactions modifiant l'utilisation de la lumière, de l'eau et des nutriments. La conception d'un tel système doit donc optimiser l'utilisation de ces ressources en maximisant les interactions positives (facilitations) et en minimisant celles négatives (compétitions). Nous définissons le problème de verger-maraîcher comme un problème d'allocation des arbres et des cultures dans les dimensions spatio-temporelles. Nous proposons trois formulations mathématiques : modèle quadratique en variables binaires (BQP), modèle linéaire en variables mixtes (MILP) et modèle linéaire en variables binaires (01LP). Les limites des méthodes exactes pour résoudre ce problème sont présentées, montrant la nécessité d'appliquer des méthodes approchées, capables de résoudre des systèmes à grande échelle avec des solutions de bonne qualité en temps raisonnable. Pour cela, nous avons développé un solveur open source, baryonyx, qui est une version parallèle de l'heuristique de Wedelin (généralisée). Nous avons utilisé l'analyse de sensibilité pour identifier les paramètres les plus influents. Une fois trouvés, nous avons fixé les autres et utilisé un algorithme génétique pour régler les plus importants sur un ensemble d'instances d'entraînement. Le jeu de paramètres optimisé peut alors être utilisé pour résoudre d'autres instances de plus grande taille du même type de problème. baryonyx avec son réglage automatique obtient des résultats améliorant l'état-de-l'art sur des problèmes de partitionnement. Les résultats sont plus mitigés sur le problème de verger-maraîchage, bien que capable de passer à l'échelle
Mixed fruit-vegetable cropping systems (MFVCS) are a promising way of ensuring environmentally sustainable agricultural production systems in response to the challenge of being able to fulfill local market requirements. They combine productions and make a better use of biodiversity. These agroforestry systems are based on a complex set of interactions modifying the utilization of light, water and nutrients. Thus, designing such systems requires to optimize the use of these resources : by maximizing positive interactions (facilitations) and minimizing negative ones (competitions). To reach these objectives, the system's design has to include the spatial and temporal dimensions, taking into account the evolution of above- and belowground interactions over a time horizon. For that, we define the MFVCAP using a discrete representation of the land and the interactions between vegetable crops and fruit trees. We formulate the problem as three models : binary quadratic program (BQP), mixed integer linear programming (MILP) and binary linear programming (01LP). We explore large models using exact solvers. The limits of exact methods in solving the MFVCS problem show the need for approximate methods, able to solve a large-scale system with solutions of good quality in reasonable time, which could be used in interactive design with farmers and advisers. We have implemented a C++ open-source solver, called baryonyx, which is a parallel version of a (generalized) Wedelin heuristic. We used a sensitivity analysis method to find useful continuous parameters. Once found, we fixed other parameters and let a genetic optimization algorithm using derivatives adjust the useful ones in order to get the best solutions for a given time limit. The optimized configuration could be used to solve larger instances of the same problem type. Baryonyx got competitive results compared to state-of-the-art exact and approximate solvers on crew and bus driver scheduling problems expressed as set partitioning problems. The results are less convincing on MFVCS but still able to produce valid solutions on large instances
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Portz, Gustavo. "Use of crop canopy sensors in the measurement of sugarcane parameters aiming site-specific nitrogen fertilization management." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-17092015-101022/.

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Plant canopy sensors have emerged as a new tool for in field on-the-go spatially localized prediction of plant biomass and nitrogen (N) uptake by crops in an indirectly and plant indestructible way as base for N variable rate fertilization. Sensors based on the combination of specific reflectance bands from the electromagnetic spectrum constitute the vast majority of canopy sensors, and this principle has already been validated in many crops. Alternatively to this concept, the use of ultrasonic distance sensors to measure crop canopy height has been presented as an option to estimate biomass. Based on that, the aim of this thesis was to validate and refine canopy sensor systems on automated diagnosis of plant parameters aimed the application of N fertilizer according sugarcane needs. Therefore, it was necessary to: 1) validate the prior calibration made for the reflectance sensor (Portz et al., 2012) and to establish the best time to use the sensor over the crop; 2) test the use of the reflectance sensor in comparative strips trials of uniform and sensor based N variable rate application testing algorithms with positive and negative slope and measuring productivity at the end of the season; 3) obtain the relationship between crop canopy height with accumulated biomass and N uptake by the crop during the initial growing season; 4) explore the plant height measured with an ultrasonic sensor comparing the results of biomass and N uptake prediction with those obtained with the reflectance sensor. The experiments were conducted on commercial sugarcane fields, and in strips of the crop with N variable rate application. The experiments were installed over clayey and sandy soils in dry and rainy seasons being all evaluated with the reflectance sensor Yara N-Sensor model ALS (N-Sensor® ALS, Yara International ASA) and partly in comparison with an ultrasonic sensing system Polaroid 6500 (Polaroid, Minnetonka, MN, USA), when the crop had stalk height between 0.2 and 0.9 m. The reflectance sensor calibration fitted with the previous published data showing the interval between 0.3 - 0.5m as the most appropriate to use this sensor over sugarcane. The positive slope algorithm was superior to the negative, except in the situation of clayey soil in rainy season where the response from the negative slope algorithm was higher. The sugarcane plant height was highly correlated with biomass and N uptake by the crop, being possible to estimate the plants canopy height indirectly by the use of an ultrasonic sensor. Comparing the sensor systems, canopy reflectance was better in the early stages of crop as canopy height was more suitable for estimating the cultural parameters when the plants already covered soil in between the rows (+ 0.6 m stalk height), being the sensor systems complementary when fertilization is widely spread in the early crop growth period.
Sensores de dossel tem se apresentado como uma nova ferramenta para a predição espacialmente localizada, em tempo real, de forma indireta e indestrutiva da biomassa vegetal e extração de nitrogênio (N) pelas culturas como base para a aplicação de fertilizantes nitrogenados em taxas variáveis. Sensores baseados na combinação de faixas específicas de reflectância do espectro eletromagnético constituem a grande maioria dos sensores de dossel sendo este princípio já validado para uso em muitas culturas. Alternativamente a este conceito, a medição da altura do dossel cultural com o uso de sensor ultrassónico se apresenta como uma alternativa para a estimativa de biomassa vegetal. Com base nisso o objetivo desta tese foi de validar e aperfeiçoar sistemas sensores para a automação do diagnóstico visando à aplicação de fertilizante nitrogenado em função da necessidade da cana-de-açúcar. Para tanto, foi necessário: 1) validar a previa calibração feita ao sensor de reflectância (PORTZ et al., 2012) assim como estabelecer o melhor momento para uso do sensor na cultura; 2) ensaiar o uso do sensor em faixas comparativas entre taxa fixa e variável testando algoritmos de aplicação com inclinação positiva e negativa para dose de N mensurando produtividade; 3) obter a relação entre altura de dossel da cultura com, biomassa acumulada e extração de nitrogênio pela planta; 4) explorar a altura de plantas mensurada com um sensor ultrassónico comparando os resultados de predição de biomassa e extração de nitrogênio com aqueles obtidos com sensor de refletância. Os experimentos foram conduzidos em talhões comerciais de cana-de-açúcar e em forma de faixas da cultura, com aplicação em taxa variada de doses de N. Os experimentos foram instalados em solos de textura argilosa e arenosa nas épocas seca e chuvosa do ano sendo todos avaliados com o sensor Yara N-Sensor, modelo ALS (N-Sensor® ALS, Yara International ASA), e em parte comparando com um sistema sensor ultrassónico Polaroid 6500 (Polaroid, Minnetonka, MN, EUA) quando a cultura apresentava altura de colmos entre 0,2 e 0,9 m. Os dados coletados para a calibração do sensor de reflectância se encaixaram exatamente aos dados já publicados mostrando-se o intervalo entre 0.3 e 0.5 m o mais indicado ao uso deste sensor. O algoritmo com inclinação positiva se mostrou superior ao negativo exceto na situação de solo argiloso em estação chuvosa onde a resposta do algoritmo negativo foi maior. A altura de planta de cana-de-açúcar se mostrou altamente correlacionável com biomassa e extração de nitrogênio pela cultura, sendo possível estimar a altura do dossel das plantas de forma indireta pelo uso do sensor ultrassónico. Comparando-se os sistemas sensores, reflectância de dossel se mostrou melhor em estádios iniciais da cultura enquanto altura de dossel se mostrou mais indicada para estimar os parâmetros culturais quando as plantas já recobriam as entrelinhas (+0.6 m colmo), mostrando-se os sistemas sensores complementares quando o período de fertilização for mais amplo na fase inicial da cultura.
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Books on the topic "Crop parameter"

1

Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. Radar Remote Sensing for Crop Biophysical Parameter Estimation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5.

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Baron, William R. Growing season parameter reconstructions for New England using killing frost records, 1697-1947. Orono, Me: Maine Agricultural and Forest Research Station, 1996.

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Pal, S. K. Impact of climatic parameters on agricultural production and minimizing crop productivity losses through weather forecast and advisory service in SAARC countries. Dhaka: SAARC Agriculture Centre, 2012.

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Byrne, Robert James. An evaluation of the effect of nitrogen management programmes on plant nitrogen concentration in milling wheat and subsequent yield and quality parameters. Dublin: University College Dublin, 1998.

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Weather and Rice: Proceedings of the International Workshop on the Impact of Weather Parameters on Growth and Yield of Rice 7-10 April, 1986. Agribookstore, 1987.

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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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Book chapters on the topic "Crop parameter"

1

Akhter, Shamim, Keigo Sakamoto, Yann Chemin, and Kento Aida. "Parameter-Less GA Based Crop Parameter Assimilation with Satellite Image." In Computational Science and Its Applications – ICCSA 2009, 118–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_9.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Radar Vegetation Indices for Crop Growth Monitoring." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 177–228. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_7.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Biophysical Parameter Retrieval Using Compact-Pol SAR Data." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 155–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_6.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Introduction." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 1–6. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_1.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Vegetation Models: Empirical and Theoretical Approaches." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 37–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_3.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Evolution of Semi-empirical Approach: Modeling and Inversion." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 73–106. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_4.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Biophysical Parameter Retrieval Using Full- and Dual-Pol SAR Data." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 107–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_5.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Basic Theory of Radar Polarimetry." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 7–35. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_2.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Summary and Conclusions." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 229–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_8.

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Chen, Yuting, Samis Trevezas, and Paul-Henry Cournede. "Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction." In 2013 Proceedings of the Conference on Control and its Applications, 67–74. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973273.10.

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Conference papers on the topic "Crop parameter"

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Revill, Andrew, Anna Florence, Steve Hoad, Bob Rees, Alasdair MacArthur, and Mathew Williams. "UAV-Based Approaches for Crop Parameter Retrievals." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518284.

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Shastry, Aditya, Sanjay H A, and Madhura Hegde. "A parameter based ANFIS model for crop yield prediction." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154708.

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Yingying Dong, Jinkai Zhang, Zhijie Wang, Karl Staenz, Craig Coburn, Wei Xu, Xiaodong Yang, and Jihua Wang. "Method to speed up LUT-based crop canopy parameter mapping." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946874.

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Tian, Xin, Erxue Chen, Zengyuan Li, Z. Bob Su, Feilong Ling, Lina Bai, and Fengyu Wang. "Comparison of crop classification capabilities of spaceborne multi-parameter SAR data." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651326.

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Kang, MengZhen, XianWen Wang, Rui Qi, and Philippe de Reffye. "GreenScilab-Crop, an open source software for plant simulation and parameter estimation." In 2009 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC). IEEE, 2009. http://dx.doi.org/10.1109/ossc.2009.5416863.

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Sagues, Lluis, Xavier Fabregas, and Antoni Broquetas. "Crop height monitoring and surface parameter estimating using polarimetric and interferometric radar techniques." In Europto Remote Sensing, edited by Manfred Owe, Guido D'Urso, and Eugenio Zilioli. SPIE, 2001. http://dx.doi.org/10.1117/12.413938.

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Varella, Hubert, Martine Guerif, and Samuel Buis. "Global Sensitivity Analysis (GSA) Measures the Quality of Parameter Estimation. Case of Soil Parameter Estimation with a Crop Model." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779531.

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Schmullius, C., and T. Schrage. "Classification, crop parameter estimation and synergy effects using airborne DLR E-SAR and DAEDALUS images." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702810.

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Guo, Yiqing, Feng Zhao, Yanbo Huang, Matthew A. Lee, Krishna N. Reddy, Reginald S. Fletcher, Steven J. Thomson, and Jianxi Huang. "Early detection of crop injury from glyphosate by foliar biochemical parameter inversion through leaf reflectance measurement." In 2013 Second International Conference on Agro-Geoinformatics. IEEE, 2013. http://dx.doi.org/10.1109/argo-geoinformatics.2013.6621891.

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Turin, E. N., K. G. Zhenchenko, A. A. Gongalo, V. Yu Ivanov, N. V. Karaeva, and V. V. Reent. "The results of the study of the direct seeding in the Research Institute of Agriculture of Crimea." In CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE. Federal State Budget Scientific Institution “Research Institute of Agriculture of Crimea”, 2020. http://dx.doi.org/10.33952/2542-0720-2020-5-9-10-49.

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The research aimed to study the influence of different tillage-and-planting systems on the soil density of chernozem southern in the central steppe of the Crimea. The soil density is a very important parameter both in the direct seeding and conventional tillage since the no-tillage crop production system is that left soil undisturbed. The stationary experimental site is situated in the village of Klepinino Krasnogvardeyskiy district Republic of Crimea (Department of Field Сrops, FSBSI “Research Institute of Agriculture of Crimea”). This report provides data for 2019. Even though the direct seeding does not include topsoil loosening, the soil density parameters are optimal (1-1.4 g/cm3) in the 0-10-centimeter layer for the development of the roots of the studied crops. In the 10-20 and 20-30 cm layers, the soil in the reporting period is a little over-compacted despite the farming system
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