Potato Research最新文献

{"title":"Nuclear Inclusion Protein B-Driven In Vitro Replication System for Potato Virus Y","authors":"Jiayu Fu, Zichen Li, Minjun Liu, Guowei Geng","doi":"10.1007/s11540-024-09791-4","DOIUrl":"https://doi.org/10.1007/s11540-024-09791-4","url":null,"abstract":"<p>Potato virus Y (PVY) is a significant pathogen affecting potato and other crops globally. Current research on PVY primarily concentrates on strain analysis and protein interactions with the host. However, studies on PVY replication in vitro remain limited, primarily due to the absence of a dedicated in vitro replication system for PVY. This study aimed to develop an in vitro replication system mediated by the nuclear inclusion protein B (NIb) of PVY. Through IPTG gradient experiments, we determined that the optimal expression concentration of MBP-NIb is 0.2 mM. The results indicate that the optimal temperature for NIb expression in <i>Escherichia coli</i> is 37 °C, yielding approximately 10% solubility, which is sufficient for subsequent affinity chromatography using the MBP tag. The purified MBP-NIb specifically recognizes the 3′ terminal region of the positive-sense RNA and the 5′ terminal region of the negative-sense RNA of PVY in vitro, facilitating the synthesis of complementary strands of the viral RNAs. To our knowledge, this is the first successful establishment of an in vitro replication system for PVY. This system will advance the study of PVY replication mechanisms.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expression of Jaburetox 2-Ec in Potato Encodes Resistance Against Tuta absoluta Meyrick (Lepidoptera: Gelechiidae)","authors":"Merve Tekinsoy, Allah Bakhsh, Mehmet Emin Çalışkan","doi":"10.1007/s11540-024-09788-z","DOIUrl":"https://doi.org/10.1007/s11540-024-09788-z","url":null,"abstract":"<p>The tomato leaf miner <i>Tuta absoluta</i> Meyrick is the most serious pest of potato worldwide. In the present study, we explored an alternate approach to induce resistance in potatoes against <i>T. absoluta</i> by engineering Jaburetox-2Ec, an insecticidal peptide derived from an isoform of urease from Jack bean (<i>Canavalia ensiformis</i> L.). The construct was developed by cloning a PCR-amplified fragment of <i>Jaburetox-2Ec</i> (270 bp) in a pBIN61 vector under the control of a 35S promoter and was named pBIN-<i>JBTX</i>. Furthermore, the recombinant plasmid expressing Jaburetox-2Ec was transformed into potato cv. Lady Olympia using <i>Agrobacterium tumefaciens</i> strain EHA105. The results showed the proper integration of the T-DNA region in primary transformants. Quantitative PCR assays showed the independent transgenic lines had enhanced transcript levels of Jaburetox. The leaf biotoxicity assays were performed by releasing pre-starved 2nd, 3rd, and 4th instar larvae of <i>T. absoluta</i> on transgenic potato plants. As a result, depending on the larval stage, the mortality rate of larvae feeding on transgenic plants ranged from 11 to 33%. Additionally, it was found that larvae feeding on control plants gained more weight than those feeding on transgenic plants. Besides that, deformities in larvae feeding on transgenic plants were also recorded. Overall, the results exhibited the insecticidal activity of <i>Jaburetox 2-Ec</i> against <i>T. absoluta.</i> The transgenic lines can be used as a source of germplasm for an efficient potato breeding programme.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"48 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Quest for the Sli Locus","authors":"Ernst-Jan Eggers, Ying Su, Sjaak A. W. van Heusden, Michiel E. de Vries, Christian W. B. Bachem, Richard G. F. Visser, Pim Lindhout","doi":"10.1007/s11540-024-09792-3","DOIUrl":"https://doi.org/10.1007/s11540-024-09792-3","url":null,"abstract":"<p>Genetic gain in potato breeding is limited by the heterozygous tetraploid genome of cultivated potato. Recent efforts to breed potato at the diploid level promise to improve genetic gain and allow more straightforward genetics and introgression breeding. Diploid F1 hybrid potato breeding relies on the ability to create diploid inbred lines via repeated self-fertilization. However, self-fertilization of diploid potato is hampered by a gametophytic self-incompatibility system encoded by the S-locus that prevents fertilization by self-pollen. Nonetheless, self-compatible diploid potato genotypes exist and have been used to create inbred lines. The <i>S-locus inhibitor</i> (<i>Sli</i>) gene is a dominant gene that provides strong self-compatibility in diploid potato and was previously mapped to Chromosome 12. While the <i>Sli</i> gene has already been identified and characterized, the most tedious challenge was to develop the optimal phenotyping methods and genetic populations preceding the cloning of this gene. To this end, we developed an effective phenotyping protocol to identify suitable parents and create diploid populations segregating for <i>Sli</i>. We show that an accurate phenotyping method is crucial to discriminate between confounding fertility factors and self-compatibility. In addition, we found that the <i>Sli</i> locus shows extreme segregation distortion on Chromosome 12. Finally, we used these insights to develop three F1 populations that segregate for <i>Sli</i>, which we later used for the identification of the <i>Sli</i> gene.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"58 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"N-BEATS Deep Learning Architecture for Agricultural Commodity Price Forecasting","authors":"G. H. Harish Nayak, Md Wasi Alam, G. Avinash, K. N. Singh, Mrinmoy Ray, Rajeev Ranjan Kumar","doi":"10.1007/s11540-024-09789-y","DOIUrl":"https://doi.org/10.1007/s11540-024-09789-y","url":null,"abstract":"<p>Agricultural commodity prices have unique characteristics and tend to fluctuate more due to seasonality, inelastic demand, and production uncertainty. Additionally, the considerable volatility observed in time series data amplifies the complexity, presenting a notable challenge. This paper addresses the intricate challenges associated with forecasting agricultural commodity prices, which are characterized by seasonality, inelastic demand, and production uncertainty. We introduce deep learning (DL) models to navigate the complexities of nonlinear and nonstationary price data in the agricultural sector. Despite the success of DL models in handling intricate data, their original design for tasks like image processing and natural language processing necessitates specialized architectures for time series forecasting. To meet this demand, we evaluate the neural basis expansion analysis for interpretable time series forecasting (N-BEATS) model, a novel architecture designed specifically for time series forecasting, on weekly potato price data collected from the Farrukhabad market in Uttar Pradesh between January 2003 and August 2023. A comparative analysis is conducted with three other models: convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) using the same dataset. Various forecasting evaluation criteria, including root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), are employed to assess and compare the models’ performance. Empirical results demonstrate that the N-BEATS model consistently outperforms the other models across all evaluation criteria. Furthermore, the Diebold–Mariano (DM) test confirms the significant forecasting advantage of the N-BEATS model over the other sequential models. This research showcases the potential of the N-BEATS model in enhancing the precision of agricultural commodity price forecasting, with implications for stakeholders such as farmers and planners. The findings contribute to advancing the understanding of deep learning applications in the agricultural domain, offering a promising avenue for more accurate and reliable forecasting methods.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"116 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotypic Diversity of Released South African Bred Potato Varieties for Tuber Yield and Processing Quality","authors":"Ntombokulunga W. Mbuma, Philippus J. Steyn, Sunette M. Laurie, Maryke T. Labuschagne, Michael W. Bairu","doi":"10.1007/s11540-024-09790-5","DOIUrl":"https://doi.org/10.1007/s11540-024-09790-5","url":null,"abstract":"<p>Potato (<i>Solanum tuberosum</i> L.) is a nutritious starchy tuber crop consumed as a staple food in most potato growing countries. Its productivity and production are limited by climate change; thus, it is of utmost importance to tap into the diversity of germplasm resources, therefore, diversifying the potato varieties available for production. The objectives of this study were to determine the phenotypic diversity among the selected Agricultural Research Council-Vegetable, Industrial and Medicinal Plants (ARC-VIMP) potato varieties for tuber yields and processing quality and to determine the correlation between measured traits. Two imported and 22 local potato varieties were planted in three different environments in South Africa. Significant (<i>P</i> &lt; 0.001) differences in the tested varieties across all environments were observed for all measured traits. High broad-sense heritability (&gt; 0.6) was observed for most of the traits, except for fry colour and the yield of unmarketable tubers. Nine ARC varieties and three standard varieties were associated with high tuber yield and quality traits, indicating their suitability for the fresh market and processing industry. Variety Mondial and five ARC varieties were associated with high tuber yield, indicating their suitability for the fresh market. Varieties released between 2004 and 2021 had high tuber yield, dry matter and specific gravity compared to the standard varieties and varieties released between 1980 and 1999, although there were slight differences in the magnitudes of their traits measured. These varieties could be recommended for commercial production for specific markets and for use as potential parents for further genetic improvement of the potato crop.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"85 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Planting Date and Field Type outweighed the Effect of Seed Origin on Ware Potato Yield","authors":"Arie Pieter Paulus Ravensbergen, Chunmei Zou, Paul C. Struik, Pytrik Reidsma, Corné Kempenaar, Martin K. van Ittersum","doi":"10.1007/s11540-024-09794-1","DOIUrl":"https://doi.org/10.1007/s11540-024-09794-1","url":null,"abstract":"<p>Potato yields in the Netherlands are highly variable. Although variability in yield has previously been attributed to several crop management factors, part of the variability remains unexplained. It is hypothesized that part of this unexplained yield variability is influenced by seed potato origin, encompassing where and under what conditions seed potatoes have been cultivated. In this study, we investigated the effect of seed potato origin on crop characteristics and tuber yield in interaction with and in comparison to planting date and field type in a two-year on-farm experiment on a large-scale potato farm. We evaluated three different seed potato origins of the variety Fontane, three different planting dates (early, intermediate, late), and two different field types (wet rainfed and dry irrigated). Origin significantly affected the number of stems per plant and the number of tubers per stem in both years. This resulted in a significant effect of origin on the number of tubers per plant in the first year. In that year, the origin with the lowest number of tubers per plant also produced the highest yield of tubers larger than 50 mm. Despite these (small) effects of seed potato origin on crop characteristics, origin did not significantly affect gross and marketable yield. Moreover, there was no interaction between origin and planting date and/or field type. However, planting date and field type had a significant effect on yield. Ware potato yield in the wet rainfed field was up to 17 t ha⁻¹ higher than in the dry irrigated field, and late planting resulted in a yield reduction of up to 10 t ha⁻¹. We conclude that for maximizing ware potato yield of the variety Fontane in the Netherlands, the effects of planting date and field type outweighed the effects of seed tuber origin.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Germination Time on the Morphological, Structural, Vibrational, Thermal and Pasting Properties of Potato Starch from Solanum tuberosum Phureja Group","authors":"Juan Carlos Lucas-Aguirre, Víctor Dumar Quintero-Castaño, Johan Sebastián Henao-Ossa, Oscar Yael Barrón-García, Mario Enrique Rodríguez-García","doi":"10.1007/s11540-024-09784-3","DOIUrl":"https://doi.org/10.1007/s11540-024-09784-3","url":null,"abstract":"<p>This work focuses on the study of the physicochemical changes that take place during a short germination period in flours and starches of the Creole potato tuber. To this end, the changes in the composition of flours and the structural, thermal, vibrational, functional and pasting changes of the isolated starches from germinated potatoes were evaluated during the 12-day germination period, measured every 4 days. The water absorption index (WAI) and the swelling powder showed no significant changes. Germination resulted in a decrease in fat and ash content, but an increase in protein and amylose content. Scanning electron microscopy (SEM) showed no changes in the morphology of the starch during germination. X-ray diffraction showed that isolated Creole potato starch contains nanocrystals with hexagonal crystal structure, which are not affected by germination. Differential scanning calorimetry (DSC) shows a shift of the gelatinization peak to the right, which could be attributed to the concentration effect. The pasting profiles of the isolated starches show no significant changes, indicating that the starch granules do not suffer any external damage due to the enzymatic process during germination and that the final viscosity behaves like a hydrogel.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial Biofortification of Iron in Solanum tuberosum L. through Siderophore-Producing Rhizobacteria along with L-Tryptophan and Iron Sulphate Supply","authors":"Zain Mushtaq, Mehwish Liaquat, Sarvet Jehan, Muhammad Tahir Akram, Muhammad Tauseef Jaffar, Abdulrahman Alasmari","doi":"10.1007/s11540-024-09785-2","DOIUrl":"https://doi.org/10.1007/s11540-024-09785-2","url":null,"abstract":"<p>Food security necessitates not only increasing crop yields but also enhancing food quality while simultaneously mitigating the detrimental effects of agricultural practices on natural resources and the environment. Iron (Fe) deficiency is a prevalent nutritional concern that affects a significant portion of the global population, particularly children and pregnant women. One potential solution to address these deficiencies is microbial biofortification, which involves the intentional augmentation of beneficial nutrients within agricultural plants. Plant growth–promoting rhizobacteria (PGPR) can enhance the Fe content in the edible parts of plants through various direct and indirect methods. This study aimed to assess the combined impact of two bacterial isolates, both individually and in conjunction with L-tryptophan and iron sulphate, on the growth, physiology, tuber characteristics, and Fe content of potatoes (<i>Solanum tuberosum </i>L<i>.</i>). The findings indicated that the application of PGPR and plant growth regulators (PGR) significantly enhanced plant height, haulm yield, and the number of tubers per plant. Fe content was notably increased by the sole application of L-tryptophan (24.58%) and bacterial strains (Z-20 increased by 17.54% and Z-37 by 31.05%). Furthermore, the introduction of the microbial consortia and L-tryptophan resulted in a remarkable enhancement, with up to a two-fold increase in the Fe concentration of potatoes compared to the control. Additionally, nitrogen (N) and phosphorus (P) concentrations also increased significantly. The results indicate that the presence of rhizobacteria can facilitate nutrient absorption by plants from the soil. This study demonstrates that the combined use of microbial-assisted biofortification and PGR is a promising, economically viable strategy for addressing micronutrient deficiencies, especially in resource-limited countries.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"46 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative Network, Technological Progress and Potato Production in China","authors":"Huaiyu Wang, Qiaoyu Wang, Yao Xiao, Hongshu Chen, Zhiwen Su, Chao Xiang","doi":"10.1007/s11540-024-09780-7","DOIUrl":"https://doi.org/10.1007/s11540-024-09780-7","url":null,"abstract":"<p>The development of science and technology is one of the vital driving forces for the Chinese agricultural development. Based on 2,513 valid potato invention patents from 1986 to 2019 in China, a social network analysis is applied to investigate the effects of the network changes in potato patents on the innovation activities and potato production. The results show that China has formed a status quo of separation between potato-producing areas and patent-invention places. The collaborative relationships of potato patents are mainly distributed in non-main production areas. Most of the collaborations are intra-provincial rather than inter-provincial. The correlation relationship between potato innovation and potato yield is positive. But the density and cluster coefficients of collaborative networks in potato research affect the potato innovations differently. Considering the variation in collaborative networks and different technology progress in potato production across regions, a cross-regional exchange platform is needed to promote the dissemination and extension of potato patents in the main potato-producing areas.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"5 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture","authors":"Burak Gülmez","doi":"10.1007/s11540-024-09786-1","DOIUrl":"https://doi.org/10.1007/s11540-024-09786-1","url":null,"abstract":"<p>This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}