European Journal of Agronomy最新文献

{"title":"Citrus pose estimation under complex orchard environment for robotic harvesting","authors":"","doi":"10.1016/j.eja.2024.127418","DOIUrl":"10.1016/j.eja.2024.127418","url":null,"abstract":"<div><div>The growth poses of citrus on trees are diverse. To ensure minimal loss during citrus harvesting, accurately estimating the pose of citrus is particularly important. To solve this problem, this research developed a real-time citrus pose estimation system based on neural networks and point cloud processing algorithms. Specifically, this method uses neural networks to identify citrus. After constructing the citrus point cloud, it is input into the Random Sample Consensus with Levenberg-Marquardt (RANSAC-LM) point cloud processing algorithm to obtain the citrus coordinates. Combined with citrus growth information, the pose is output. By analyzing the distribution of citrus poses, citrus poses convenient for end- effector harvesting are defined. To enhance the camera's ability to obtain information about citrus, a camera observation model is constructed to dynamically adjust the camera position. Through experiments, the appropriate deep learning target detection framework YOLO V5 is selected for citrus object detection. The precision (P), recall rate (R), and mean average precision (mAP) are 92.3 %, 79.1 %, and 88.5 % respectively. This network can handle detection tasks in real orchard environments. The original Random Sample Consensus (RANSAC) is improved by using the Levenberg-Marquardt (LM) nonlinear optimization method. Experimental results show that RANSAC-LM reduces the citrus center coordinate precision error from (0.2, 0.2, 2.3) mm to (0.1, 0.2, 1.4) mm, reduces the accuracy Spherical Error Probable (SEP) from 2.77 to 1.61, and finally reduces the citrus pose error from 5.72° to 2.43°. The efficiency of the proposed citrus pose estimation algorithm is 0.24 s. Deployed on a citrus picking robot, it verifies the feasibility of the algorithm and provides a new solution for the pose estimation problem of citrus harvesting robots.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shallow drains and straw mulch alleviate multiple constraints to increase sunflower yield on a clay-textured saline soil I. Effects of decreased soil salinity, waterlogging and end-of-season drought","authors":"","doi":"10.1016/j.eja.2024.127416","DOIUrl":"10.1016/j.eja.2024.127416","url":null,"abstract":"<div><div>A well-designed drainage system can alleviate soil salinity and waterlogging, leading to increased crop yield if the drainage does not cause a water shortage late in the growing season. We conducted three field experiments with sunflower across two dry seasons (Experiment I in 2019–20, and II and III in 2020–21) in a tropical landscape to examine the effectiveness of shallow drains and mulch in overcoming these constraints. In Experiment I, four surface drains of 0.1 or 0.2 m depth spaced 1.2 or 1.8 m apart were tested along with an undrained treatment. In Experiment II, the same four drainage treatments and an undrained treatment in the main plots were split into mulch (-M and +M) sub-plots. Experiment III had four main treatments, undrained, surface drains (SD; 0.1 m deep, 1.8 m apart), subsoil drains (SSD; 0.5 m deep, 4.5 m apart) and SSD+SD each split for mulch (-M and +M) sub-plots. At vegetative emergence and at the 8-leaf stage, all plots were inundated (3–5 cm depth; EC_w: 1.5–2.5 dS m^–1) for 24 h before opening the drains. Drainage treatments without mulch reduced SEW₃₀ (waterlogging index, sum of excess water within 30 cm soil depth) and soil EC_1:5 at 0–15 cm, while increasing sunflower yield by 15–100 % compared to the undrained no-mulch treatment. Relative to the undrained no-mulch treatment, drains with straw mulch conserved soil water, reduced EC_1:5 at 0–15 cm and increased yield in Experiments II and III by 40–47 and 76–143 %, respectively. There were no yield differences among the combinations of shallow drains. Although combined drains (SSD+SD) added 25–30 % extra yield relative to surface drains, these have higher installation costs. Shallow surface drains at 1.2–1.8 m spacing coupled with mulch are effective options for smallholder farmers to reduce salinity, waterlogging and drought stresses, and increase yield on saline, clay soils.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the temperature sensitivity of rice (Oryza sativa L.) yield and its components in China using the CERES-Rice model","authors":"","doi":"10.1016/j.eja.2024.127419","DOIUrl":"10.1016/j.eja.2024.127419","url":null,"abstract":"<div><div>The effects of temperature changes on rice (<em>Oryza</em> sativa L.) yield and its components have been widely documented. However, most existing studies are based on small-scale, short-term field experiments, with few assessing these effects on a large scale or over long periods. Here, the calibrated Crop Environment Resource Synthesis (CERES)-Rice model was used for numerical simulations over six climate regions in the major rice cultivation areas of China for the period of 1989–2018. The simulated results were used to estimate the temperature sensitivity of rice yield with a panel model in each climate region, and the yield sensitivity was then decomposed into the temperature sensitivity of three components: panicle number per unit area (Pan_no), filled grain number per panicle (Grain_no), and grain weight (Grainwt). Results indicated that rice yield exhibited negative temperature sensitivity across all climate regions, driven primarily by the temperature sensitivity of Grain_no in most regions. Additionally, Grainwt did not vary with temperature in all regions. Further analysis suggested that yield, Pan_no, and Grain_no were more sensitive to high temperature degree days (HDD) than to growing degree days (GDD). Under the warmer scenarios, HDD increase played a dominant role in the reduction of Grain_no, while the joint effect of GDD and HDD resulted in an increased Pan_no in most regions. However, the negative effect of temperature on Grain_no outweighed its positive effect on Pan_no, leading to a decline in yield. This study provides insight for understanding the temperature response of rice yield and its components and will be beneficial for developing targeted adaptations to ensure rice sustainable production under global warming.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growth conditions but not the variety, affect the yield, seed oil and meal protein of camelina under Mediterranean conditions","authors":"","doi":"10.1016/j.eja.2024.127424","DOIUrl":"10.1016/j.eja.2024.127424","url":null,"abstract":"<div><div>European agriculture policies emphasize the importance of agricultural sustainability, focusing on increase of biodiversity through crop diversification. In Mediterranean dryland cropping systems, the introduction of crops in rotation with cereals is challenged by scarce precipitation and high evapotranspiration. In this scenario, camelina (<em>Camelina sativa</em> (L.) Crantz), a low-input annual oleaginous crop with a high morphological plasticity, short life cycle, and interesting oil and meal composition, could be an option to be included in rotation with winter cereals. The aim of this experiment was to study the agronomic performance, and seed oil and meal protein contents of camelina in two different climatic conditions, with a sowing delay in one of them. Several trials were conducted in Montargull (Mediterranean semihumid) and in Lleida (Mediterranean semiarid) in two seasons (2020–21 and 2021–22). In Montargull, two sowing dates (November, SD1 and January, SD2) were established. In each growing condition, three spring camelina varieties were sown (<em>Calena, CO46</em> and <em>GP204</em>). Camelina was harvested between May and July, and yield and harvest index were measured. After cold pressing the seeds, seed oil and meal protein contents were analysed. Camelina yield and quality was not related to the variety, but to two climatic scenarios: 1) a favourable rainfall distribution without important drought periods (2020–21); 2) significant rainfalls in November and April, but with a drought period in between (2021–22). In the first situation, camelina production ranged from 1533 to 2187 kg ha⁻¹, with high seed oil (40.4–41.4 %) and meal protein (41.0–44.8 %) contents. In the second situation, the yield decreased to 242–661 kg ha⁻¹, seed oil content to 31.0–34.7 %, and meal protein content to 37.6–40.4 %. Despite these seasonal differences, SD1 in Montargull obtained higher average yields and protein content than in Lleida and in SD2. In contrast, in Lleida and in SD2 in Montargull camelina produced higher oil content. The implementation of camelina into Mediterranean dryland crop rotation systems is feasible. Considering the importance of moisture in these climatic conditions, the use of no-till practices is recommended in dryland fields to avoid excessive water loss, while the use of camelina in irrigated fields could be explored. However, more long-term agronomic and industrial research is still needed.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding increased grain yield and water use efficiency by plastic mulch from water input to harvest index for dryland maize in China’s Loess Plateau","authors":"","doi":"10.1016/j.eja.2024.127402","DOIUrl":"10.1016/j.eja.2024.127402","url":null,"abstract":"<div><div>In China’s Loess Plateau, plastic mulch (PM) is an effective agronomic practice for dryland maize (<em>Zea mays</em> L.) to increase grain yield (GY) and water use efficiency (WUE) under water-limited conditions. However, there is dearth of quantitative data on how PM affects field water use step by step, subsequently increasing GY and WUE. The study aimed to identify which changes in the field water use pathway generated the positive effects of PM on GY and WUE. During the early vegetative stage (EVS), late vegetative stage (LVS), reproductive stage (RS), and entire growing season (GS), the field water use pathway was divided into five sequential steps: total water input (TWI), evapotranspiration to TWI ratio (ET/TWI), transpiration to ET ratio (T/ET), transpiration efficiency (TE), and harvest index (HI). A seven-year field experiment demonstrated that although TWI_GS exhibited no change, TWI_LVS and TWI_RS increased by 6.7 % and 5.4 %, respectively, on average following PM application. This highlighted the PM’s ability to increase water input into fields. Overall, PM negatively, neutrally, and positively affected ET/TWI_EVS (−29.8 %), ET/TWI_LVS, and ET/TWI_RS (+23.9 %), respectively, and thereby made unchanged ET/TWI_GS. There were average increases of 83.3 %, 29.8 %, 26.1 %, and 33.9 % by PM for T/ET_EVS, T/ET_LVS, T/ET_RS, and T/ET_GS respectively. Therefore, increased diversion of inputted water to T occurred in fields with PM. TE positively responded to PM during the LVS and RS. PM increased TE_LVS by 20.9 % and TE_RS by 44.1 % on average, signifying increased aboveground biomass produced per unit T under PM. The proportion of aboveground biomass partitioned to grains remained unaffected by PM as indicated by the neutral response of HI to PM. Increased water input into fields, diversion of inputted water to T, and aboveground biomass produced per unit T contributed to increased GY (+19.9 %) and WUE (+20.0 %) after applying PM. The study enhances our understanding of improved field water use pathway to produce more grains using limited water supplies in PM-based drylands in China’s Loess Plateau and similar regions worldwide.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wheat growth stage identification method based on multimodal data","authors":"","doi":"10.1016/j.eja.2024.127423","DOIUrl":"10.1016/j.eja.2024.127423","url":null,"abstract":"<div><div>Accurate identification of crop growth stages is a crucial basis for implementing effective cultivation management. With the development of deep learning techniques in image understanding, research on intelligent real-time recognition of crop growth stages based on RGB images has garnered significant attention. However, the small differences and high similarity in crop morphological characteristics during the transition between adjacent growth stages pose challenges for accurate identification. To address this issue, this study proposes a multi-scale convolutional neural network model, termed MultiScalNet-Wheat (MSN-W), which enhances the algorithm's ability to learn complex features by utilizing multi-scale convolution and attention mechanisms. This model extracts key information from redundant data to identify winter wheat growth stages in complex field environments. Experimental results show that the MSN-W model achieves a recognition accuracy of 97.6 %, outperforming typical convolutional neural network models such as VGG19, ResNet50, MobileNetV3, and DenseNet. To further address the difficulty in recognizing growth stages during transition periods, where canopy morphological features are highly similar and show small differences, this paper introduces an innovative approach by incorporating sequential environmental data related to wheat growth stages. By extracting these features and performing multi-modal collaborative inference, a multi-modal feature-based wheat growth stage recognition model, termed MultiModalNet-Wheat (MMN-W), is constructed on the basis of the MSN-W model. Experimental results indicate that the MMN-W model achieves a recognition accuracy of 98.5 %, improving by 0.9 % over the MSN-W model. Both the MSN-W and MMN-W models provide accurate methods for observing wheat growth stages, thereby supporting the scientific management of winter wheat at different growth stages.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination with moderate irrigation water temperature and nitrogen application rate enhances nitrogen utilization and seed cotton yield","authors":"","doi":"10.1016/j.eja.2024.127417","DOIUrl":"10.1016/j.eja.2024.127417","url":null,"abstract":"<div><div>To promote the efficient utilization of groundwater and improve nitrogen fertilizer effectiveness, a reasonable range of nitrogen application rates and irrigation water temperature was investigated. A field experiment was conducted in Xinjiang, China, in 2022 and 2023, involving four irrigation water temperature levels (T0: 15 °C, T1: 20 °C, T2: 25 °C, and T3: 30 °C) and three nitrogen application rates (F1: 250 kg ha⁻¹, F2: 300 kg ha⁻¹, and F3: 350 kg ha⁻¹). The results indicated that soil nitrogen content, cotton dry matter weight, cotton nitrogen content, seed cotton yield, and nitrogen partial factor productivity (NPFP) increased with higher nitrogen application rates. However, as irrigation water temperature increased, soil nitrogen content decreased, whereas cotton dry matter weight, cotton nitrogen content, seed cotton yield, and NPFP initially increased before declining. Notably, the maximum yield and NPFP among all treatments were observed in T2F2 (25 °C, 300 kg ha⁻¹), yielding 6652 kg ha^–1 and 6941 kg ha^–1, and in T2F1 (25 °C, 250 kg ha^–1), with 24.20 kg kg^–1 and 25.20 kg kg^–1 in 2022 and 2023, respectively. Furthermore, the optimal range of irrigation water temperature of 23.82–27.41 °C and nitrogen application rate of 276.43–289.23 kg ha^–1 were identified to achieve over 80 % of the highest seed cotton yield and NPFP using multiple regression and spatial analysis methods. This study offers valuable guidance for optimizing irrigation and fertilization strategies to enhance resource efficiency and promote sustainable cotton production in arid regions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Input uncertainty in CSM-CERES-wheat modeling: Dry farming and irrigated conditions using alternative weather and soil data","authors":"","doi":"10.1016/j.eja.2024.127401","DOIUrl":"10.1016/j.eja.2024.127401","url":null,"abstract":"<div><div>In the current study, the uncertainties of wheat modeling using gridded soil and weather datasets were analyzed under dry farming and irrigated conditions. In this regard, the performance of the CSM-CERES-Wheat model forced with different weather-soil data combinations was studied in some dryland regions in Iran based on normalized Root Mean Square Error (nRMSE), Kling-Gupta Efficiency (KGE), and Percent Bias (PBIAS). The data combination scenarios were W_S-S_O: soil observations and gridded weather datasets including ERA5-Land (W_E-S_O) and the combinations of non-precipitation ERA5-Land forcings with CHIRPS (W_CE-S_O) and PERSIANN-CDR (W_PE-S_O), SoilGrids250m gridded soil data and weather observations (W_O-S_S), and soil and weather observations (W_O-S_O). Although the CHIRPS-ERA5L improved simulations relative to ERA5-Land and PERSIANN-CDR-ERA5-Land, there was still an nRMSE greater than 30 %, a KGE below 0.50, and an absolute PBIAS exceeding 25 % for dry farming yield in most drylands under W_S-S_S and W_S-S_O, indicating significant input uncertainties. The high uncertainty in dry farming wheat yield under W_S-S_S and W_S-S_O can be attributed to the uncertainties in simulating the water stress index in CSM-CERES-Wheat. The dry farming wheat yield was, however, simulated satisfactorily with SoilGrids250m products for W_O-S_S. The dry farming wheat yield showed the largest sensitivity to the uncertainty in precipitation forcing. The notable uncertainty in water stress simulation, and therefore in dry farming yield, appears to stem from the high uncertainty in precipitation products. These findings demonstrate that dry farming modeling is subject to notable input uncertainty when reliable meteorological records are lacking in our study area. SoilGrids250m can be reliably used to model wheat yield under dry farming conditions in the study area when weather observations are available. However, the applicability of SoilGrids250m largely depends on the availability of regional soil observations. Irrigated wheat yield was successfully simulated due to the reduced uncertainty in water stress. Therefore, using alternate weather-soil data provides a robust solution to data unavailability when wheat water requirements are sufficiently met. Nonetheless, caution is needed when using gridded weather datasets to force the CSM-CERES-Wheat model for dry farming.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on tomato disease image recognition method based on DeiT","authors":"","doi":"10.1016/j.eja.2024.127400","DOIUrl":"10.1016/j.eja.2024.127400","url":null,"abstract":"<div><div>Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The nitrogen nutrition index as a tool to assess nitrogen use efficiency in potato genotypes","authors":"","doi":"10.1016/j.eja.2024.127397","DOIUrl":"10.1016/j.eja.2024.127397","url":null,"abstract":"<div><div>Enhancing nitrogen (N) use efficiency (NUE) is crucial for the sustainable production of potatoes (<em>Solanum tuberosum</em> L.). The aims of this study were to assess i) the genotypic variation of the main components of NUE (N utilization efficiency (NUTE) and N recovery efficiency (NRE)), ii) the association between these components, related traits, and cultivars, and iii) the usefulness of N nutrition index (NNI) to assess NUTE and NRE of potato genotypes grown under different levels of N availability. Two field experiments were carried out in Chile during the season 2021–2022. Treatments were the combination of 15 potato cultivars and three rates of N (0, 200, and 400 kg N ha⁻¹). High variations were observed in total dry matter biomass (DM) (5.9–22.1 Mg ha⁻¹), tuber DM biomass (5.1–18.3 Mg ha⁻¹), total N concentration (1.01–2.24 %), total N uptake (98–323 kg ha⁻¹), NUTE (35–91 kg tuber DM kg⁻¹ N), and NRE (−14–54 %). Total N uptake was significantly related to total DM biomass and traits related to N concentration and N uptake. In both experiments, strong negative correlations were observed between total N concentration and NUTE (<em>r</em> = −0.95 – −0.98). Also, NUTE and N harvest index were positively correlated. The relationship between NUTEtub and NNI was well described (<em>p</em> &lt; 0.01; <em>R</em>^<em>2</em> = 0.55–0.87) by a negative power function. The predicted average of NUTEtub for a NNI = 1 (optimal N status) showed a narrow range (49.5–56.9 kg DM kg⁻¹ N). Both relative tuber yield and relative total biomass were significantly related to NNI (<em>R</em>^<em>2</em> = 0.56 and 0.66). The cultivar Desiree, Karu-INIA, and Shepody were among the cultivars with the highest NNI. A significant positive relationship (<em>p</em> &lt; 0.01; <em>R</em>^<em>2</em> = 0.42) was observed between NRE and NNI. This study demonstrates the effectiveness of the NNI in evaluating and interpreting NUTE and NRE based on genotype and nitrogen supply, ultimately enhancing decision support for improving NUE in potato production systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}