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

Evaluation of the GreyWater Footprint Comparing the Indirect Effects of Different Agricultural Practices

Increasing global food demand and economic growth result in increasing competition over scarce freshwater resources, worsened by climate change and pollution. The agricultural sector has the largest share in the water footprint of humanity. While most studies focus on estimating water footprints (WFs) of crops through modeling, there are only few experimental field studies. The current work aims to understand the effect of supposedly better agricultural practices, particularly precision agriculture (variable rate application of fertilizers and pesticides) and conservation agriculture (minimum, strip, or no-tillage), on water deterioration and water pollution. We analyzed the results from an experimental field study in the northeast of Italy, in which four different crops are grown across three years of crops rotation. We compared minimum, strip, and no-tillage systems undergoing variable to uniform rate application. Grey WFs are assessed based on a field dataset using yield maps data, soil texture, and crop operations field. Leaching and associated grey WFs are assessed based on application rates and various environmental factors. Yields are measured in the field and recorded in a precision map. The results illustrate how precision agriculture combined with soil conservation tillage systems can reduce the grey water footprint by the 10%. We assessed the grey Water Footprint for all the field operation processes during the three-year crop rotation

AN EXAMPLE OF DEVELOPING COVARIATES FOR PROBLEMS IN PRECISION AGRICULTURE

Methodology for precision agriculture is, perhaps, too focused on methods that allow for spatial correlation in the ANOVA error term. While sound inference about differences between local yields can be computed, no understanding of what is driving these differences is achieved. A completely general form for a spatial model can include suitable covariates. Most research in precision agriculture includes gathering a variety of site-specific information. Through the presentation of the analysis of data from a published soybean [Glycine max (L.) Merr.] study, one specific type of covariate is developed - a duration index for soybean canopy light interception over the growing season. The relationship of the index to grain yield is reasonably well determined (R² = 0.82). We, therefore, suggest that the quest for modeling an appropriate covariate or covariates is primary. Treating spatial variation by other methods should only be used when the quest has failed

Integrating environmental models and precision agriculture data to identify spatially explicit subfield opportunities for increased sustainability and economic return

From a general vantage point, mechanized agriculture cropping system fields such as those producing maize, wheat, and soybean appear to homogenous in terms of yield and economic return. However, the availability of precision agriculture data has revealed subfield variability in yield and economic performance as well as environmental impact. While subfield spatial and temporal variability of yields is known to exist and can be characterized using yield monitor or remote sensing technology, how to best use these data sources to better improve both economic and environmental performance remains challenging. The following dissertation describes the integration of environmental and economic modeling tools with precision agriculture data and public databases to identify subfield areas where the adoption of more sustainable practices is both environmentally impactful as well as cost-effective. Chapter 1 of the dissertation describes the development of a precision agro-economic and environmental performance tool. To assess the tool performance, modeled data was compared with empirical NO3--N leaching data obtained from a long-term experiment. Results of the comparison showed the model captured spatial variability of NO3--N leaching at the subfield spatial scale with an average RMSE of 21.5 kg ha-1 and an r2 of 0.19. A case study analysis of a cropping system field using the modeling framework revealed estimated NO3--N leaching and ROI were correlated, and high priority zones with low ROI and high NO3- leaching were found to represent approximately 6% of the total field area. Chapter 2 focuses on the application of the precision agro-economic and environmental modeling framework described in Chapter 1. Analysis of 15 fields showed a significant correlation between N-loss and economic return indicating a majority of fields contain areas susceptible to limited ROI and high NO3- leaching and/or N2O emissions. Simulating the targeted integration of switchgrass in these areas was estimated to reduce field-scale NO3--N leaching by up to 21.1% and , however the economic impacts were dependent on potential biomass prices which were predicted to approximately $93 t-1 yr-1 in order to reach relative break-even compared with maize and soybean cropping. Chapter 3 describes the novel use of the ApSIM agriculture system simulator and public data sources as a tool for estimating economically optimum seeding and N-fertilizer application rates at field to subfield scales. Maximum crop productivity typically corresponded with maximum seeding and N-fertilizer rates, however maximum ROI often corresponded with reduced input resources, particularly seeding density. Modeled crop production loss between maximum yield and maximum ROI seeding and N-management scenarios ranged from 313.2 to 538.7 kg ha-1 and corresponded with an ROI increases ranging from 5.5 to 11.0%. Results indicated yield-oriented seeding and N-fertilizer recommendations decrease potential ROI

Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset