FertiliCalc: A Decision Support System for Fertilizer Management

Villalobos, Francisco J.; Delgado, Antonio; López-Bernal, Álvaro; Quemada, Miguel

doi:10.1007/s42106-019-00085-1

Cited by 24 publications

(14 citation statements)

References 20 publications

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“…The calculation of N recommended rates is based on a mass balance to be performed at the field scale. Recently, a decision support system was developed to help farmers and agronomists calculate nutrient requirements for a crop rotation designed by the user by picking from 149 crops (Villalobos et al, 2020). This application, called ‘FertiliCalc’, calculates the N rates for the selected crops and allows the user to choose from a combination of straight and complex mineral fertilisers and organic compounds.…”

Section: Resultsmentioning

confidence: 99%

Comparison of nitrogen fertilisation recommendations of West European Countries

Jordan‐Meille,

Denoroy,

Dittert

et al. 2023

European J Soil Science

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Nitrogen (N) budgets at farm level are influenced by N fertilisation recommendations. In this study, we reviewed and analysed the underlying principles and methods of N fertilisation recommendations in ten West European countries, to identify similarities and differences, and develop suggestions for reconsideration and improvement. An analysis of national official documents on N fertilisation recommendations revealed that there were three main categories of calculation methods: (i) ‘N mass balances’ (France, Italy, Spain), (ii) ‘Corrected standards’ (Germany, Netherlands, Switzerland, Luxembourg), and (iii) ‘Pre‐parameterised calculations’, which rely on a soil N supply typology (United Kingdom, Ireland, Belgium). In total 16 variables were identified in the calculation methods. The more complex methods use 10 (Italy, France), while the simplest only rely on 3 (Luxembourg). The most common variables include the availability of N in manure, the N uptake by a crop, and the N released by crop residues. Few countries explicitly consider N losses to ground and surface waters or to the atmosphere in the calculation methods. In some countries, the N fertilisation recommendation has a voluntary status, in other countries a legal one (caps on maximum allowable N rates). We compared the N fertiliser recommendations for a wheat crop grown on a farm with livestock, and for a farm with a diverse arable crop rotation without livestock. Across the 10 countries, large differences in the N fertilisation calculation methods and resulting N recommendations existed for the two management scenarios, ranging from almost no fertilisation to 135 kg N ha‐1, and from 111 to 210 kg N ha‐1, respectively. The differences were not accounted for by the complexity of the equations used, but rather resulted from contrasting reference values for N availability in manure, N uptake by crop and N leaching.However, the study concluded that standardisation of the method to calculate N fertilisation recommendations is likely to be counterproductive as there are no objective reasons to favour one method more than the others. Nonetheless, improvements in N use efficiency are necessary. Farm scale mass balance, combined with parameters such as minimum residual soil mineral N test at harvest was suggested as being an important consideration.This article is protected by copyright. All rights reserved.

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Section: Resultsmentioning

confidence: 99%

Comparison of nitrogen fertilisation recommendations of West European Countries

Jordan‐Meille,

Denoroy,

Dittert

et al. 2023

European J Soil Science

Self Cite

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“…The literature does not provide any studies on the agronomic effects of DSSs with similar objectives. DSSs with similar objectives are considered to be that by Pagán et al [17], by Bueno-Delgado et al [16], Smart Fertilizer Management [15], and by Villalobos et al [33]. They also have a clear focus on the least-cost combination of fertilizers, and consider at least nutrients nitrogen, phosphorus, and potash in parallel.…”

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

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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.

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“…Fertiliser is a substance of synthetic or natural origin having some essential elements that improve plant growth. However, fertilisers are one of the major sources of pollution in surface and groundwater when used improperly [86]. In addition, the increased use of inorganic fertilisers, mostly N-based, has a significant contribution to agricultural GHGs emissions [1,86].…”

Section: Fertilisation and Fertigationmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

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FertiliCalc: A Decision Support System for Fertilizer Management

Cited by 24 publications

References 20 publications

Comparison of nitrogen fertilisation recommendations of West European Countries

Comparison of nitrogen fertilisation recommendations of West European Countries

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

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

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