Download or read book Sub field Crop Yield and Yield Stability Analysis with Multi source Datasets and Advanced Crop Simulation Model written by Guanyuan Shuai and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The combination of precision agriculture technology and big data allows farmers to improve the sustainability in crop production by improving profit and minimizing environmental loss. However, knowledge about managing sub-field yield variations over the years (yield stability) is less understood, so the actual adoption rate of precision technology is low, especially in small farms. The overarching goal of this dissertation was to evaluate the spatial and temporal variability of maize and soybean yields related to landscape characteristics and management in the U.S. Midwest (Chapter 1).Chapter 2 aims to develop a yield stability map using satellite images for ten maize/soybean main production states in the Corn Belt. This study uses Landsat images over eight years and common land unit polygons as the primary dataset to classify each field into stable high, stable low, and unstable areas. I further quantified nitrogen (N) balance and loss and associated monetary and environmental loss for each yield stability class. Across a wide range of crop growth environments, this large-scale analysis has shown reliable and consistent subfield yield patterns that could improve fertilizer application.Chapter 3 investigates the potential of these low-yielding areas in supporting biofuel crop (switchgrass) production to replace current crops and subsequent improvement in soil and climate change. I also proposed planting a cover crop, rye, to evaluate the effect on the soil nitrate level. The switchgrass and cover crop production were simulated by the Systems Approach to Land Use Sustainability (SALUS) model, which also modeled soil organic matter dynamics under the proposed and current (continuous maize or maize/soybean rotation) scenarios. Benefits such as reduced soil nitrogen, improved soil structure, and higher heterogeneity of landscape composition demonstrate the potential of yield stability maps to support sustainable agricultural production. In Chapter 4, I combined the yield stability maps with remote sensing images to improve the maize yield prediction at subfield scale. In this study, I first made a further division in the unstable area by including the topographic features and modeled cumulative crop water stress. The modeled water stress was combined with Landsat Analysis Ready Dataset (ARD) to predict maize yield using a random forest algorithm. Results showed that incorporating the modeled water stress with vegetation index derived from Landsat images produced the highest accuracy in estimated maize yield compared to satellite images alone. This work emphasized the yield stability map's ability to capture subfield spatial variations of soil and plant available water stress information.Chapter 5 compares the accuracy in yield level estimation between yield stability maps and high-resolution Planet images (3-m, bi-weekly) and analyzes the temporal changes of spatial variation of crop conditions during the growing season. The yield stability maps could predict similar maize yield levels in stable zones compared to satellite images. The temporal analysis in the spatial variation indicated greater changes in the field at the beginning or end of the season, while spatial patterns of crop growth dynamics remain stable in the mid-season. Early- or late-season changes did not always impact the final yield. This analysis provided an in-depth analysis of the in-season image so farmers could better monitor crops in real-time. Conclusions from these research projects and recommendations on interpretation yield and satellite images for sustainable production are presented in Chapter 6.

Download or read book Yield gap analysis of field crops written by Food and Agriculture Organization of the United Nations and published by Food & Agriculture Org.. This book was released on 2018-06-29 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: To feed a world population that will exceed 9 billion by 2050 requires an estimated 60% increase over current primary agricultural productivity. Closing the common and often large gap between actual and attainable crop yield is critical to achieve this goal. To close yield gaps in both small and large scale cropping systems worldwide we need (1) definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short and long term); (2) identification of the causes of gaps between yield levels; (3) management options to reduce the gaps where feasible and (4) policies to favour adoption of sustainable gap-closing solutions. The aim of this publication is to critically review the methods for yield gap analysis, hence addressing primarily the first of these four requirements, reporting a wide-ranging and well-referenced analysis of literature on current methods to assess productivity of crops and cropping systems.

Download or read book Modeling Physiology of Crop Development Growth and Yield written by Afshin Soltani and published by CABI. This book was released on 2012-01-01 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model studies focus experimental investigations to improve our understanding and performance of systems. Concentrating on crop modelling, this book provides an introduction to the concepts of crop development, growth, and yield, with step-by-step outlines to each topic, suggested exercises and simple equations. A valuable text for students and researchers of crop development alike, this book is written in five parts that allow the reader to develop a solid foundation and coverage of production models including water- and nitrogen-limited systems.

Download or read book Maximizing Crop Yields written by N. K. Fageria and published by CRC Press. This book was released on 1992-03-27 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Details the physiological, agronomical, and environmental factors needed to maintain or increase the productivity and sustainability of agricultural systems. Addressed to scientists in the agriculture industry, and graduate and advanced undergraduate students, rather than to farmers. Explores the ba

Download or read book Working with Dynamic Crop Models written by Daniel Wallach and published by Elsevier. This book was released on 2006-05-10 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap. * State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond

Download or read book A tool for climate smart crop insurance Combining farmers pictures with dynamic crop modelling for accurate yield estimation prior to harvest written by Singh, B. K. and published by Intl Food Policy Res Inst. This book was released on 2019-01-08 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study found that dynamic crop models have the accuracy to predict normal to high yields, but there are limits to their ability to capture low yields. On the other hand, the machine learning (CNN) model has better ability to capture lower yields. It is worth noting that the crop model only took into consideration mainly the weather data to predict yields; it is handicapped by the paucity of detailed management information deployed by farmers. However, the pictures sent by farmers reflected more yield-determining characteristics that reflected crop health and yield and that were then captured by the CNN. Finally, among the picture characteristics parameters, if “GCC & H” correlations are high, this could be a good indicator of low yield.

Download or read book Crop Growth Simulation Modelling And Climate Change written by M. Mohanty and published by Scientific Publishers. This book was released on 2015-06-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on “Crop Growth Simulation Modelling and Climate Change”. A group of authors have dealt with different aspects of crop modelling viz., Crop growth simulation models in agricultural crop production, Applications of Crop Growth Simulation Models in Climate Change Assessments, Biophysical impacts and priorities for adaptation of agricultural crops in a changing climate, Climate change projections – India’s Perspective, Impact of Rising Atmospheric CO2 concentration on Plant and Soil processes, Modelling the impact of climate change on soil erosion in stabilization and destabilization of soil organic carbon, Simulating Crop Yield, Soil Processes, Greenhouse Gas Emission and Climate Change Impacts with APSIM, InfoCrop Model, CropSyst model and its application in natural resource management, Climate change and crop production system: assessing the consequences for food security, A biophysical model to analyze climate change impacts on rainfed rice productivity in the mid-hills of Northeast India, AquaCrop Modelling: A Water Driven Simulation Model, Conservation Agriculture: A strategy to cope with Climate Change, Effect of climate change on productivity of wheat and possible mitigation strategies using DSSAT model in foot hill of Western Himalayas, Integrating Remote Sensing Data in Crop Process Models, Climate change impact assessment using DSSAT model, Decision Support System for Managing Soil Fertility and Productivity in Agriculture, De-Nitrification De-Composition Model - An Introduction for SOC Simulations, Crop Simulation Modeling for Climate Risk assessment: Adaptation and Mitigation Measures and Rules of Simulations, Rothamsted Carbon (RothC) Model and its Application in Agriculture etc.

Download or read book Tutorials in Chemoinformatics written by Alexandre Varnek and published by John Wiley & Sons. This book was released on 2017-06-22 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: 30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods. Tutorials in Chemoinformatics contains more than 100 exercises in 30 tutorials exploring key topics and methods in the field. It takes an applied approach to the subject with a strong emphasis on problem-solving and computational methodologies. Each tutorial is self-contained and contains exercises for students to work through using a variety of software packages. The majority of the tutorials are divided into three sections devoted to theoretical background, algorithm description and software applications, respectively, with the latter section providing step-by-step software instructions. Throughout, three types of software tools are used: in-house programs developed by the authors, open-source programs and commercial programs which are available for free or at a modest cost to academics. The in-house software and data sets are available on a dedicated companion website. Key topics and methods covered in Tutorials in Chemoinformatics include: Data curation and standardization Development and use of chemical databases Structure encoding by molecular descriptors, text strings and binary fingerprints The design of diverse and focused libraries Chemical data analysis and visualization Structure-property/activity modeling (QSAR/QSPR) Ensemble modeling approaches, including bagging, boosting, stacking and random subspaces 3D pharmacophores modeling and pharmacological profiling using shape analysis Protein-ligand docking Implementation of algorithms in a high-level programming language Tutorials in Chemoinformatics is an ideal supplementary text for advanced undergraduate and graduate courses in chemoinformatics, bioinformatics, computational chemistry, computational biology, medicinal chemistry and biochemistry. It is also a valuable working resource for medicinal chemists, academic researchers and industrial chemists looking to enhance their chemoinformatics skills.

Download or read book Analysis of Yield and the Influencing Soil Factors Using Site specific and Modeling Techniques in Two Four crop rotation Fields in Sacramento Valley California written by Jorge Francisco Perez and published by . This book was released on 2001 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Working with Dynamic Crop Models written by Daniel Wallach and published by Academic Press. This book was released on 2013-11-25 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. 50% new content – 100% reviewed and updated Clearly explains practical application of the methods presented, including R language examples Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields

Download or read book Detecting the Effect of Dust and Other Climate Variables on Crop Yields Using Diagnostic Statistical Crop Models written by Alexis Hoffman and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Food security and agriculture productivity assessments require a strong understanding of how climate and other drivers influence regional crop yields. While the effects of temperature, precipitation, and carbon dioxide are relatively well-understood, the effect of dust on crop yields has yet to be thoroughly investigated. This line of inquiry is warranted because many areas of the world with frequent dust storms and high dust loadings are often food insecure, and because wind erosion is prevalent in the High Plains of the United States, a major crop-producing area. Existing research suggests that the effect of dust on yields should be largely negative, but until now this has not been investigated on a regional scale. A major hindrance to understanding the effect of dust on crop yields is insufficient data and inadequate methods of analysis. In this dissertation, we developed data and analysis methods for three distinct projects to determine whether dust affects regional crop yields. In the first project, we validated the use of random forest, a machine learning technique, as a diagnostic crop model that can be used to assess the impact of individual climate predictors on yields. Because we motivated this research with food security concerns, we analyzed climate signals in the crop yield record of sub-Saharan Africa from 1962-2014. From this work, we determined that random forest could function as a statistical crop model, but the data quality and resolution inhibited the ability to detect the effect of dust on yields in this area of the world. In our second line of inquiry, we shifted the focus to the central region of the United States for its high quality and high resolution data, as well as its importance as a major crop-producing region of the world. Because these data had higher temporal resolution, we could explore individual phases of the growing season. We developed crop-specific algorithms to compute the planting date, establishment phase, critical window, and grain filling phase to investigate yield responses to phase-specific climate predictors. Using these data, the random forest identified distinct phase-specific responses for important climate predictors. Finally, we computed dust metrics from three different data sources and merged them with climate and yield data in the central region of the United States to estimate the impact of dust on yields. Over the entire central US region, we found that including dust as a predictor in each crop model did not improve yield predictions for the region as a whole. However, when crop models were applied to individual states, we found several instances in which dust weakly reduced yields. Although these state-specific results were encouraging, we presented them cautiously because the yield responses could be an artifact of either partitioning the data or a true yield response that is obscured when data was spatially aggregated. While the results were largely inconclusive, we have advanced the capabilities of statistical crop modeling, developed data sets that can be used to move the science forward, and revealed new questions that merit further research.

Download or read book Advances in Crop Modelling for a Sustainable Agriculture written by Kenneth Boote and published by Burleigh Dodds Series in Agric. This book was released on 2019-10-22 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crop modelling has huge potential to improve decision making in farming. This collection reviews advances in next-generation models focused on user needs at the whole farm system and landscape scale.

Download or read book Mathematical Models of Crop Growth and Yield written by Allen R Overman and published by CRC Press. This book was released on 2019-08-30 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highlighting effective, analytical functions that have been found useful for the comparison of alternative management techniques to maximize water and nutrient resources, this reference describes the application of viable mathematical models in data analysis to increase crop growth and yields. Featuring solutions to various differential equations, the book covers the characteristics of the functions related to the phenomenological growth model. Including more than 1300 literature citations, display equations, tables, and figures and outlining an approach to mathematical crop modeling, Mathematical Models of Crop Growth and Yield will prove an invaluable resource.

Download or read book Time Series and Spatial Analysis of Crop Yield written by Yared Assefa and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.

Download or read book Yield Gains in Major U S Field Crops written by J. Stephen C. Smith and published by . This book was released on 2014 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Remote Sensing and Grid based Meteorological Datasets for Regional Soybean Crop Yield Prediction and Crop Monitoring written by Preeti Mali and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Regional crop yield estimations using crop models is a national priority due to its contributions to crop security assessment and food pricing policies. Many of these crop yield assessments are performed using time-consuming, intensive field surveys. This research was initiated to test the applicability of remote sensing and grid-based meteorological model data for providing mproved and efficient predictive capabilities for crop bio-productivity. The soybean prediction model (Sinclair model) used in this research, requires daily data inputs to simulate yield which are temperature, precipitation, solar radiation, day length initialization of certain soil moisture parameters for each model run. The traditional meteorological datasets were compared with simulated South American Land Data Assimilation System (SALDAS) meteorological datasets for Sinclair model runs and for initializing soil moisture inputs. Considering the fact that grid-based meteorological data has the resolution of 1/8th of a degree, the estimations demonstrated a reasonable accuracy level and showed promise for increase in efficiency for regional level yield predictions. The research tested daily composited Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (both AQUA and TERRA platform) and simulated Visible/Infrared Imager Radiometer Suite (VIIRS) sensor product (a new sensor planned to be launched in the near future) for crop growth and development based on phenological events. The AQUA and TERRA fusion based daily MODIS NDVI was utilized to developed a planting date estimation method. The results have shown that daily MODIS composited NDVI values have the capability for enhanced monitoring of the soybean crop growth and development with respect to soybean growth and development. The method was able to predict planting date within ±3.4 days. A geoprocessing framework for extracting data from the grid data sources was developed. Overall, this study was able to demonstrate the utility of MODIS and VIIRS NDVI datasets and SALDAS meteorological data for providing effective inputs to crop yield models and the ability to provide an effective remote sensing-based regional crop monitoring. The utilization of these datasets helps in eliminating the ground-based data collection, which improves cost and time efficiency and also provides capability for regional crop monitoring.

Download or read book Improving Crop Estimates by Integrating Multiple Data Sources written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-01-26 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: The National Agricultural Statistics Service (NASS) is the primary statistical data collection agency within the U.S. Department of Agriculture (USDA). NASS conducts hundreds of surveys each year and prepares reports covering virtually every aspect of U.S. agriculture. Among the small-area estimates produced by NASS are county-level estimates for crops (planted acres, harvested acres, production, and yield by commodity) and for cash rental rates for irrigated cropland, nonirrigated cropland, and permanent pastureland. Key users of these county-level estimates include USDA's Farm Services Agency (FSA) and Risk Management Agency (RMA), which use the estimates as part of their processes for distributing farm subsidies and providing farm insurance, respectively. Improving Crop Estimates by Integrating Multiple Data Sources assesses county-level crop and cash rents estimates, and offers recommendations on methods for integrating data sources to provide more precise county-level estimates of acreage and yield for major crops and of cash rents by land use. This report considers technical issues involved in using the available data sources, such as methods for integrating the data, the assumptions underpinning the use of each source, the robustness of the resulting estimates, and the properties of desirable estimates of uncertainty.