Processing techniques development, volume 3

The author has identified the following significant results. Analysis of the geometric characteristics of the aircraft synthetic aperture radar (SAR) relative to LANDSAT indicated that relatively low order polynominals would model the distortions to subpixel accuracy to bring SAR into registration for good quality imagery. Also the area analyzed was small, about 10 miles square, so this is an additional constraint. For the Air Force/ERIM data, none of the tested methods could achieve subpixel accuracy. Reasons for this is unknown; however, the noisy (high scintillation) nature of the data and attendent unrecognizability of features contribute to this error. It is concluded that the quadratic model would adequately provide distortion modeling for small areas, i.e., 10 to 20 miles square

Investigation and Modeling of the Optimization Potential of Adapted Nitrogen Fertilization Strategies in Corn Cropping Systems with Regard to Minimize Nitrogen Losses

Das Ziel der Arbeit war die "Untersuchung und Modellierung des Optimierungspotentials von angepassten Stickstoff-Düngungsstrategien in Mais Anbausystemen in Hinblick auf Stickstoffverluste". Der Hintergrund der Arbeit liegt in der steigenden Umweltbelastung durch die Landbewirtschaftung. Aus diesem Grund war die Dissertation in den Kontext des Graduiertenkollegs "Strategien zur Vermeidung der Emission klimarelevanter Gase und umwelttoxischer Stoffe aus Landwirtschaft und Landschaftsnutzung" an der Universität Hohenheim eingegliedert. Die Zielsetzung des Graduiertenkollegs war die Entwicklung von Methoden zur Quantifizierung und die Modellierung der Entstehung und der Emission von klimarelevanten Gasen und umwelttoxischen Stoffen aus der Landwirtschaft und Landnutzung und die ökonomische Bewertung praktikabler Vermeindungsstrategien. Um das Optimierungspotential von angepassten Stickstoff-Düngungsstrategien in Mais zu ermitteln, wurde die Arbeit wurde folgendermaßen gegliedert: 1. Untersuchung der räumlichen Variabilität und zeitlichen Stabilität von Maiserträgen auf drei Schlägen im Oberrheingraben. 2. Ermittlung der zu Grunde liegenden ertragslimitierenden Faktoren in allen Schlägen mittels einfacher und komplexer Modelle. 3. Entwicklung angepasster Stickstoffdüngestrategien unter der Berücksichtigung von Ertragsvariabilität und den ertragslimitierender Faktoren. Das Untersuchungsgebiet war im Oberrheingraben angesiedelt, welches als eine Region intensiver Maisproduktion gekennzeichnet ist. Gleichzeitig gehört die Region entlang des Rheins zu den bedeutendsten Trinkwassergebieten Europa. Daraus ergaben sich in den letzten Jahrzehnten der Konflikt zwischen intensiver Landbewirtschaftung verbunden mit hohen Einträgen an Düngemitteln auf der einen Seite und der Schutz der Grundwasservorkommen auf der anderen Seite. Die Untersuchungen wurden auf drei Praxisschläge bei Weisweil nordwestlich von Freiburg, Deutschland, durchgeführt. Auf allen drei Schlägen wurde seit 1998 Mais in Monokultur angebaut. In den Untersuchungen im Oberrheingraben konnte eine räumliche und zeitliche Variabilität der Kornerträge ermittelt werden. Die unterschiedlichen Ertragsmuster in jedem Schlag lassen ertragslimitierende Wachstumsbedingungen vermuten. Einerseits schien der Ertrag beeinflusst durch die zeitliche Variation von Sorte, Klima und Management, sowie durch räumlich Variation möglicher ertragslimitierender Faktoren, wie Nährstoff- und Wasserverfügbarkeit auf der anderen Seite. Um die Managementstrategien anzupassen, müssen die zu Grunde liegenden ertragslimitierenden Faktoren innerhalb der drei Schläge ermittelt werden. Über die erfassten Pflanzenparameter konnte die gemessene Ertragsvariabilität nicht erklärt werden, wohingegen Korrelationen zwischen Bodeneigenschaften und den ermittelten Ertragsvariabilitäten innerhalb der drei Schläge gezeigt werden konnten. Signifikante Zusammenhänge wurden zwischen Bodennährstoffen, Bodeneigenschaften und dem Ertrag ermittelt. Aufgrund dieser Ergebnisse scheinen die Bodeneigenschaften die Haupteinflussfaktoren für die gemessene Ertragsvariabilität auf den drei Schlägen im Oberrheingraben zu sein. Trotz allem konnte über einfache Regressionsmodelle nur ein Teil der Ertragsvariabilität erklärt werden. In einem nächsten Schritt wurden komplexe Wachstumsmodelle eingesetzt, um die Ertragsvariabilität innerhalb der Schläge zu simulieren und die zu Grunde liegenden Faktoren zu ermitteln. Das eingesetzte prozess-orientierte Modell APOLLO (Application of Precision Agriculture for Field Management Optimization) wurde auf Grundlagen von CERES und CROPGRO entwickelt. Innerhalb des Modells könnten unterschiedliche Bodeneigenschaften angepasst werden und somit das Modell kalibriert werden. Die Ergebnisse haben gezeigt, dass mittels APOLLO die Ertragsvariabilität gut wider gegeben werden kann. Als Ursachen für die Variabilität wurden vor allem Unterschiede in der Bodenverdichtung und der Durchwurzelbarkeit des Bodens angenommen. Die Korrelationen zwischen simuliertem und gemessenem Ertrag geben Auskunft über die Ausprägung der ertragslimitierenden Faktoren. Die Kalibrierung war unter anderem abhängig von der gewählten Größe der Grids. Kleine Grids konnte die Ertragsvariabilität stärker abbilden, wohingegen größere Grids die Ertragsmuster deutlich wiedergaben. Infolge dessen konnte eine bessere Kalibrierung des Modells erzielt werden, wenn die Erträge aus größeren Grids zu Grunde gelegt wurden. Das APOLLO-Modell wurde des Weiteren auch zur Entwicklung der Stickstoff-Düngeempfehlung eingesetzt. Über einen Zeitraum von 28 Jahren wurde die aktuelle Stickstoff Düngestrategie der Landwirte simuliert. Zusätzlich wurden über das APOLLO-Modell auch eine optimierte einheitliche und eine optimierte variable Stickstoff-Düngestrategien entwickelt. Die Düngestrategien wurden unter Berücksichtigung von langjährigen Wetterverhältnissen (28 Jahre) untersucht. Die Strategien wurden anhand von simuliertem Ertrag, simulierter Nitratauswaschung und simulierten ökonomischen Gesichtspunkten bewertet. Dabei wurde deutlich dass die angepassten Düngestrategien (optimiertes einheitliches Management und variable angepasstes Management) gegenüber der aktuellen Düngestrategie von Vorteil waren. Insbesondere dann, wenn die Düngestrategien für unterschiedliche Wetterbedingungen (Trocken, normal und nasse Jahre) entwickelt wurden. Die angepassten Düngestrategien führten zu einer Reduzierung des Reststickstoffes im Boden und somit zu einem verringerten Risiko der Nitratauswaschung. Auch für die gasförmigen Stickstoffverluste konnte in Optimierungspotential ermittelt werden. Die Ergebnisse zeigten eine verringerte kumulative Denitrifizierungsrate unter angepasster Düngestrategie verglichen mit der aktuellen Düngestrategie. Zusammenfassend kann gesagt werden, dass die Anwendung einer angepassten Düngestrategie (optimiertes einheitliches Management und variable angepasstes Management) zu einer Reduzierung von Stickstoffverlusten, in Form von Nitratauswaschung und Stickstoffemissionen führen kann. Generell, ist das Optimierungspotential aber abhängig vom jeweiligen Anbausystem und damit größer, wenn ein Anbausystem einem gesteigerten Verlustpotential für Stickstoff unterliegt.The aim of this study was the "Investigation and Modeling of the Optimization Potential of Adapted Nitrogen Fertilization Strategies in Corn Cropping Systems with Regard to Minimize Nitrogen Losses". The background for the investigation could be seen in the increasing number of environmental pollution by agricultural land use. The dissertation was embedded in the context of the Graduiertenkolleg "Strategies to Reduce the Emission of Greenhouse Gases and Environmental Toxic Agents from Agriculture and Land Use" at the University of Hohenheim. The objective of this Graduiertenkolleg was to develop methods for quantifying and modeling the origin and the emission of greenhouse gases and environmentally toxic agents from agriculture and land use and for assessing them economically in the sense of practicable avoidance strategies. In order to determine the optimization potential of adapted nitrogen fertilization strategies in corn the study was organized in the following parts: 1. Investigation of the spatial variability and temporal stability of corn grain yield on three fields in the Upper Rhine Valley. 2. Determination of underlying yield-limiting factors in each field by the use of simple and complex models. 3. Development of adapted nitrogen fertilization strategies in consideration of the yield variability and the underlying yield-limiting factors. The area of investigation was located in the Upper Rhine Valley, which is characterized as a region with intense corn cultivation. At the same time this region belongs to the most important water protection areas in Europe. Thus, a conflict between agricultural land use associated with high fertilizer inputs on one hand and the protection of water bodies on the other hand rose, because measured nitrate concentrations in the groundwater increased constantly within the last decades. The study was conducted on three farm fields in the boundary of Weisweil, which is located northwest of Freiburg, Germany. Since 1998 the three fields were planted continuously with corn. In a 7-year field experiment spatial variability and stability of yield could be indicated. The determined yield pattern in each field raised assumptions about varying growth conditions within and among the fields. Thus, on the one hand the corn yield seemed to be influenced by temporal variations in cultivar, climate and management and by spatial and temporal variation of possible yield-limiting factors like nutrient availability or water supply on the other hand. In order to optimize management strategies the underlying yield-limiting factors causing the spatial and temporal yield variability needed to be determined in these three fields. Whereas plant yield parameters did not explain the existing yield variability very well, soil characteristics were identified as the major factors affecting the observed yield variability in all three fields. Significant relationships were found between combinations of soil nutrient levels, soil characteristics and yield. Based on these results, it appeared that soil characteristics were the primary factor affecting spatial yield variability in the three farmer fields in the Upper Rhine Valley. However, some of the spatial yield variability remained unexplained by simple regression analysis. In a more complex approach crop growth models were implemented to simulate the spatial yield variability within the field and to get information about the underlying yield-limiting factors. Therefore the process-oriented crop growth model APOLLO was implemented to evaluate the causes of spatial yield variability of corn in the three fields. APOLLO (Application of Precision Agriculture for Field Management Optimization) is a precision farming decision support system, which is based on the CERES and CROPGRO family of crop growth models and includes different soil parameter to calibrate the model. In general the APOLLO model performed well in simulating spatial yield variability in the fields. The results indicated that the spatial yield variability was mainly affected by a varying restrictive layers and reduction of root growth within the three fields. The correlation between simulated and measured yields provided information about the strength of the soil parameter affecting the yield within these fields. The calibration results were influenced by the grid size. Whereas smaller grids provided more random monitor yield data, larger grids provided a more representative set of yield monitor data, due to the coverage of a larger area. Consequently, the APOLLO model performed better when yields belonging to larger grids were used for model calibration. The applicability of the APOLLO model can be extended by developing prescriptions for different management strategies and thus enhancing the possibilities of successfully implementing site-specific management strategies. Thus, APOLLO was used to simulate the current uniform nitrogen management strategy of the producers in Weisweil over a 28-year period. Additionally an optimum uniform management and an optimum variable-rate management were developed and simulated. For these strategies also the different weather pattern were taken into account. All three strategies were evaluated based on the simulated yield, the simulated leaching potential and the simulated economics. It was obvious, that variable-rate nitrogen fertilization strategies were most advantageous compared to the other strategies, especially, when the nitrogen application rates were differentiated for dry, normal and wet weather scenarios. Adapted nitrogen fertilization strategies, as optimum uniform management and variable-rate management indicated a potential to reduce the amount of nitrogen, which is left in the soil after harvest, and associated that the potential nitrate leaching was reduced. In a case study the cumulative denitrification under these weather and fertilization scenarios over the growing season was simulated. The results indicated a reduction of cumulative denitrification under adapted fertilization strategies when compared to current uniform management. Summarizing, the results of this study suggest, that the implementation of adapted fertilization strategies (especially the variable-rate management of nitrogen) could lead to a reduction of nitrogen losses, as nitrogen leaching and nitrogen emissions could be minimized. Generally, the optimization potential for adapted nitrogen fertilizer strategies (optimum uniform management and variable-rate management) could be improved for cropping systems that were associated with higher risk for nitrogen losses

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Crop Production Technologies

Crop production depends on the successful implementation of the soil, water, and nutrient management technologies. Food production by the year 2020 needs to be increased by 50 percent more than the present levels to satisfy the needs of around 8 billion people. Much of the increase would have to come from intensification of agricultural production. Importance of wise usage of water, nutrient management, and tillage in the agricultural sector for sustaining agricultural growth and slowing down environmental degradation calls for urgent attention of researchers, planners, and policy makers. Crop models enable researchers to promptly speculate on the long-term consequences of changes in agricultural practices. In addition, cropping systems, under different conditions, are making it possible to identify the adaptations required to respond to changes. This book adopts an interdisciplinary approach and contributes to this new vision. Leading authors analyze topics related to crop production technologies. The efforts have been made to keep the language as simple as possible, keeping in mind the readers of different language origins. The emphasis has been on general descriptions and principles of each topic, technical details, original research work, and modeling aspects. However, the comprehensive journal references in each area should enable the reader to pursue further studies of special interest. The subject has been presented through fifteen chapters to clearly specify different topics for convenience of the readers

Analysis of spatial yield variability and economics of prescriptions for precision agriculture: a crop modeling approach

Non-uniformity of soil properties, soil moisture and rooting depth, and other factors such pest and disease pressures can cause significant soybean and corn yield variability within a field. In this study, two crop growth models were used to characterize factors that cause spatial yield variability in soybeans and corn, and to evaluate economic consequences of variable rate management prescriptions. Analysis of yield data from 224 grids within a 16-hectare field in Boone, Iowa focused on water stress effects using CROPGRO-Soybean and CERES-Maize models for soybean and corn, respectively. Water stress explained 69% of the variability in soybean, and population and water stress explained 57% of corn yield variability. Grid-level optimum nitrogen fertilizer rate prescriptions for corn were also developed. Distribution of optimum nitrogen fertilizer prescription was highly spatially varied. Optimum nitrogen rates were found to range from 141 to 160 kg ha-1 in 64 of 224 grids (28.6%) which are typical fertilizer rates farmers apply for corn in Iowa. Based on model predictions, grid-level nitrogen fertilizer management used lower amounts of nitrate, produced higher yields and was more profitable than either transect- or field-level (single rate) fertilizer application. In another study, four factors affecting soybean yield variability namely, water stress, soybean cyst nematode (SCN), soil pH, and weeds, were examined in each of 100 grids within a 20-hectare field in Perry, Iowa using the CROPGRO-Soybean model. Average estimated yield loss due to the combined effects of water stress, SCN, pH, and weeds in each 0.2-hectare grid was 842 kg ha-1. Water stress had the biggest impact on soybean yield with an average yield reduction of 626 kg ha-1. Yield impact and economic consequences of three strategies namely, variable plant population density (PPD), soybean cyst nematode (SCN) resistant and susceptible varieties, and irrigation management schemes, were evaluated using 34 years of weather data. Implementing the best PPD for each year produced higher grid-level soybean yield and net return compared to using the 34-year average optimum rate. Achieving maximum net return may not be possible on a yearly basis due to uncertainties in weather condition. Using a SCN-resistant variety resulted in significant yield increase over that of a susceptible variety. Several grids had a significant increase (\u3e350 kg ha-1 ) in average yield with some grids having as much as 995 kg ha -1 (17 bu ac-1) yield increase when a SCN-resistant variety was used. Irrigating when available soil moisture reached a value of 40% and 50% significantly increased average field-level soybean yields by 1585 and 1619 kg ha-1, respectively. Excluding the cost of equipment, irrigation would significantly increase net return

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0