Evaluation of sensor-based field-scale spatial application of granular N to maize

Evangelou, Eleftherios; Stamatiadis, Stamatis; Schepers, James S.; Glampedakis, A.; Glampedakis, M.; Dercas, Nicholas; Tsadilas, Christos; Nikoli, Thomai

doi:10.1007/s11119-019-09705-2

Cited by 12 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This savings potential is mainly based on the least-cost combination of fertilizers, at largely identical nutrient inputs. In comparison, according to [35], the savings potential of sensor-based fertilizer systems ranges from 33 to 92 EUR ha −1 , whereas manufacturing companies assume savings of 20 to 30 EUR ha −1 . At least comparable results were obtained using IoFarm without additional technical equipment.…”

Section: Discussionmentioning

confidence: 99%

IoFarm in Field Test: Does a Cost-Optimal Choice of Fertilization Influence Yield, Protein Content, and Market Performance in Crop Production?

Tröster

Sauer

2021

Agriculture

View full text Add to dashboard Cite

Decision-support system (DSS) IoFarm was developed to identify economically optimal fertilizer strategies on the farm level. The average cost savings are 66 EUR ha−1. This study aimed to determine whether this approach impacts yield, protein content, and market performance in crop production compared to usual farm-fertilization strategies. Few DSSs for fertilizer optimization consider multiple nutrients. DSSs with a clear focus on both fertilizer intensity and the least-cost combination of fertilizers are even rarer. To the best of our knowledge, there is no information in the literature on the impact of such DSSs on yield, protein content, and market performance for cereal–maize crop rotation. This study determines for the first time whether the financial benefits of using such an optimization tool are in conflict with important agronomic goals. In a three-year field trial, IoFarm was compared to standard farm-fertilization strategies. Results were evaluated with an analysis of variance followed by post hoc tests. No significant differences in yield, protein content, and market performance were found for comparable fertilization variants (with or without organic fertilization). However, differences exist in the selection of fertilizers and the timing of fertilization. Results show the agronomic comparability of IoFarm and usual farm-fertilizer strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

IoFarm in Field Test: Does a Cost-Optimal Choice of Fertilization Influence Yield, Protein Content, and Market Performance in Crop Production?

Tröster

Sauer

2021

Agriculture

View full text Add to dashboard Cite

show abstract

“…Management zones (MZs) have been delineated based on a variety of data: soil chemical properties [ 7 , 8 ], soil physical properties [ 9 , 10 ], apparent soil electrical conductivity (ECa) [ 11 ], historical yield data [ 12 ], satellite imagery [ 4 ], and others [ 13 ]. For medium- and small-scale farms, soil sampling is key to soil nutrient management [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

Yuan

Shen

Yost

et al. 2022

Plants

View full text Add to dashboard Cite

Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management.

show abstract

An Overview of Precision Agricultural Technologies for Crop Yield Enhancement and Environmental Sustainability

Farid

Mustafa

Khan

et al. 2023

Climate Change Impacts on Agriculture

View full text Add to dashboard Cite

Evaluation of sensor-based field-scale spatial application of granular N to maize

Cited by 12 publications

References 34 publications

IoFarm in Field Test: Does a Cost-Optimal Choice of Fertilization Influence Yield, Protein Content, and Market Performance in Crop Production?

IoFarm in Field Test: Does a Cost-Optimal Choice of Fertilization Influence Yield, Protein Content, and Market Performance in Crop Production?

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

An Overview of Precision Agricultural Technologies for Crop Yield Enhancement and Environmental Sustainability

Contact Info

Product

Resources

About