https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/Head https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/assertion https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/provenance https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/pubinfo https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/assertion https://arxiv.org/abs/2403.10730 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-9487-5622 https://arxiv.org/abs/2403.10730 http://purl.org/dc/terms/creator https://orcid.org/0000-0003-2911-8558 https://arxiv.org/abs/2403.10730 http://purl.org/dc/terms/publisher https://ror.org/05bnh6r87 https://arxiv.org/abs/2403.10730 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://arxiv.org/abs/2403.10730 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://arxiv.org/abs/2403.10730 http://www.w3.org/2000/01/rdf-schema#comment In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enables the optimization of nitrogen (N) rates to maximize crop yield production and enhance agronomic use efficiency. However, existing works often neglect the consideration of responsivity to fertilizer as a factor influencing MZ determination. In response to this gap, we present a MZ clustering method based on fertilizer responsivity. We build upon the statement that the responsivity of a given site to the fertilizer rate is described by the shape of its corresponding N fertilizer-yield response (N-response) curve. Thus, we generate N-response curves for all sites within the field using a convolutional neural network (CNN). The shape of the approximated N-response curves is then characterized using functional principal component analysis. Subsequently, a counterfactual explanation (CFE) method is applied to discern the impact of various variables on MZ membership. The genetic algorithm-based CFE solves a multi-objective optimization problem and aims to identify the minimum combination of features needed to alter a site's cluster assignment. Results from two yield prediction datasets indicate that the features with the greatest influence on MZ membership are associated with terrain characteristics that either facilitate or impede fertilizer runoff, such as terrain slope or topographic aspect. Major findings:Researchers at Montana State University developed a new method for creating "management zones" in farm fields by using artificial intelligence to predict how crops will respond to nitrogen fertilizer. Unlike older methods that only look at historical yields, this approach uses a neural network to generate "N-response curves"—graphs showing how yield changes as fertilizer increases—for every spot in a field. To make the AI's decisions easier to understand, the researchers used "counterfactual explanations," which essentially ask: "What would have to change for this spot to behave differently?" The study found that terrain features like slope and soil moisture are the most important factors; for example, steep slopes often lead to fertilizer runoff, which makes those areas less responsive to treatment. This helps farmers apply fertilizer more accurately, saving money and reducing environmental impact. https://arxiv.org/abs/2403.10730 http://www.w3.org/2000/01/rdf-schema#label Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones https://arxiv.org/abs/2403.10730 https://schema.org/funder https://ror.org/02w0trx84 https://arxiv.org/abs/2403.10730 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://arxiv.org/abs/2403.10730 https://www.w3.org/ns/dcat#contactPoint john.sheppard@montana.edu https://arxiv.org/abs/2403.10730 https://www.w3.org/ns/dcat#endDate 2024 https://arxiv.org/abs/2403.10730 https://www.w3.org/ns/dcat#startDate 2023 https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/provenance https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://purl.org/dc/terms/created 2026-01-14T06:28:06.266Z https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://purl.org/nanopub/x/introduces https://arxiv.org/abs/2403.10730 https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/sig http://purl.org/nanopub/x/hasSignature UrxByElcB/zTShSOr+qUN0gZAn9vvrqJtIoSbBKHAy+89bROtm6gMLMYe84KbSsKPOeYUXTeYHK1H4pitwq2FGlUCvSLbz7MMaZtGU5C4OjmmCpnlCw9gmT5XUI90OtuYvLXYGLrN0IkgW+YDvf0wkLPxkXIp6h0rXgQpQo0nR/dwbqTXlC8gcBQE8GnhMCf5PHayCVHTVfhYoY/qsnbF2H+K2HpUSqlcpdUcIMcPmhFC7Q31L0LC2JZSDj74JCY/CopnakgFoZDby7oo7HnLGLU3l0ovjfAr+2p0Nz7mjaUhA1+cwbjO1Jenw9KzNo2BokCHvlEkztFK9RqD2o0yA== https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU https://w3id.org/np/RAtIdRPUDNxkLARl5U_GDNWRS4HUOlUvRO2wlguCH6hLU/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742