Potato Research最新文献

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 (Solanum tuberosum L.). 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.

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.

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.

Phytophthora infestans (Mont.) de Bary is a highly destructive oomycete in potato crops, and managing its impact is crucial. Host plant resistance plays a pivotal role in disease management. This study aimed to evaluate the resistance to late blight in nine preselected Chilean native potato genotypes through field and laboratory experiments, comparing them with the moderately susceptible control cultivar Désirée. The field trial spanned two seasons (2017/2018 and 2018/2019) at the same location in Southern Chile. Foliar damage percentage caused by natural infection was measured, and the relative area under the disease progress curve (rAUDPC) was calculated. The laboratory experiments included a detached leaflet assay and a tuber assay in 2019. The results indicated that seven genotypes, Azul (0.10), SN-1 (0.10), Chilca-1 (0.11), Azul Casposa (0.07), Corahila Reina (0.16), Piku (0.17) and Murta (0.24), exhibited partial resistance with significantly lower rAUDPC values than the control cultivar across both seasons. Moreover, the detached leaflets assay in 2019 highlighted four genotypes with substantially lower damage percentages: Azul Casposa (0.11%), Chilca-1 (0.64%), Piku (1.11%) and SN-1 (3.16%). The tuber assay conducted in 2019 revealed that five of the foliar-resistant genotypes, Azul (43.98%), SN-1 (38.06%), Chilca-1 (41.54%), Corahila Reina (45.5%) and Murta (39.11%), exhibited low to medium resistance also in tubers. This study successfully identified favourable variation for Phytophthora infestans resistance, indicating the potential suitability of given Chilean native potato germplasm as donors in potato breeding programs.

Chlorogenic acid (5-O-caffeoylquinic acid, CGA) is the most abundant phenolic compound in potatoes, serving as one of the material bases for potato resistance to insects and diseases. Nevertheless, excessive CGA can impact the taste of potatoes. The application of exogenous phytohormones is an effective technical approach to regulate CGA metabolism through gene expression of transcriptional regulatory pathways. Herein, we determined the content of CGA under the application of sucrose and multiple phytohormones and explored the expression changes of related genes. As a proof-of-concept, the effects of exogenous sucrose and multiple phytohormones on CGA metabolism in potato tuber flesh were investigated. The CGA content was significantly decreased in sucrose-treated potato tuber tissues, as well as in those treated with abscisic acid (ABA), salicylic acid (SA), and methyl jasmonate (MeJA). Among these treatments, the effect of SA was the most obvious. Meanwhile, the expression changes of six major enzyme genes involved in potato CGA metabolism, including phenylalanine ammonia-lyase (PAL), 4-coumarate coenzyme A ligase (4CL), hydroxycinnamoyl CoA quinate hydroxycinnamoyl transferase (HQT), hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyl transferase (HCT), ρ-coumarate 3’-hydroxilase (C3H), and caffeoyl shikimate esterase (CSE), were analyzed. Under sucrose treatment, HQT was down-regulated, whereas three genes (PAL, 4CL, and CSE) were notably up-regulated, and expressions of HCT and C3H did not show significant changes. ABA induced the expression of HCT, but inhibited the expression of PAL, 4CL, HQT, C3H, and CSE. The expression of all six enzyme genes was down-regulated under SA treatment. MeJA induced the expression of HQT, but inhibited the expression of PAL, 4CL, HCT, C3H, and CSE. Collectively, sucrose and phytohormones can regulate CGA accumulation in potato tuber flesh by regulating the expression levels of genes involved in the CGA pathway. These results provided a theoretical basis for further elucidating the transcriptional regulation mechanism of CGA metabolism.

Climate change in the form of heat waves, torrential rains and floods is showing impact on the food security in Pakistan. Potato is the main cash crop of the region affected by climate change. However, impact assessment of climate change for the potato-potato cropping system has not been studied yet. Hence, in the present study, we studied the impact of projected climate change and key adaptation options on the potato-potato cropping system using the DSSAT-CSM-SUBSTOR-Potato model. The model was calibrated using 2019 spring and fall season field experiments data, while evaluation was made using spring and fall season data of 2019 and 2020 respectively. After calibration and evaluation, model sensitivity analysis for carbon, temperature, water and nitrogen (CTWN) was performed, and after that, it was applied to determine the impact of climate warming and change in rainfall on spring and fall potato during mid-century. The results revealed that during the baseline period (1980–2020), maximum (Tmax) and minimum (Tmin) air temperature were increased by 0.7 and 0.8 °C during the spring growing season and by 0.8 and 0.9 °C during the autumn growing season, respectively. Furthermore, it was projected that autumn potato will be more influenced due to climate change than spring potato. Under future projections, temperature change for spring planted potato will be 2.7 to 3.8 °C for Tmin and 2.1 to 3.4 °C for Tmax. However for the autumn seasons, potato increase in maximum temperature will be from 2.4 to 3.6 °C and for minimum temperature the change will be from 2.7 to 4.0 °C. Simulation outcome showed that without adaptation strategies, tuber yield will be reduced from 23 to 29% and from 19 to 36% in spring and fall potato, respectively. While with adopting suitable adaptation strategies such as fertigation, planting date adjustment (earlier planting of spring potato by 15 days and delayed planting of fall by 20 days), higher thermal time requiring cultivars, increase of 12% in plant population and nitrogen quantity, tuber yield can be increased by 9 to 13% during spring and by 10 to 14% during autumn potato during mid-century. Therefore, farmers should adopt suitable adaptation strategies as mentioned to reduce the negative impact of climate change on potato yield.

Pollen viability is a limiting factor in potato hybridization, although it varies considerably among potato varieties. Exploring the molecular basis of pollen viability will give a theoretical foundation for improving hybridization. In this research, we showed that the four StLAT52 genes, namely StLAT52a, StLAT52b, StLAT52c and StLAT52d were the key genes affecting potato pollen viability, and they were only expressed in flowers. It was also noted that these genes were localized in the nucleus. An assessment of protein–protein interactions showed that StLAT52a and StLAT52b proteins, StLAT52a and StLAT52c proteins, and StLAT52b and StLAT52c proteins interacted both in vitro and in vivo. When these genes were transferred into tobacco, the number of pollen grains significantly increased, and the pollen viability increased by almost 10%. In addition, the expression of the StLAT52 genes in tobacco resulted in significant changes in genes associated with the proteasome, ribosome biogenesis and plant hormone signal transduction pathways during the flowering stage. Taken together, this research provided an in-depth understanding of the structure and functions of the LAT52 genes in potatoes and their association with pollen viability. The results of this research can be used as a basis for further exploring the molecular mechanism underlying pollen viability.

This study investigated the impact of pulsed electric fields (PEF) treatment combined with hot-air (HA) and microwave (MW) drying on the quality attributes of yellow and purple potato samples. PEF + HA and PEF + MW treatments showed no significant difference in total phenolic content for yellow potatoes. However, the raw purple sample exhibited significantly higher phenolic content compared to purple PEF1 and PEF3 samples. DPPH antioxidant activity for yellow and purple potatoes ranged from 18.48 to 33.19% with HA drying, and from 21.38 to 39.94% with MW drying. MW drying following PEF treatment was more effective in enhancing antioxidant activity compared to HA drying. However, it was observed that PEF levels had no statistical effect on total monomeric anthocyanin. PEF treatment prior to drying may be recommended in practice as it does not cause structural changes in SEM analysis. According to FTIR results, PEF and drying processes caused some changes in the chemical structure of potato samples. In the PCA results, the untreated purple HA sample exhibited characteristics that were most similar to those of the raw purple potato sample. Conversely, the yellow PEF + MW and untreated yellow with MW samples displayed properties closest to the raw yellow potato samples. According to chemometric analysis, the models exhibit high r_CV and r_Pre values coupled with low RMSEC and RMSEP values and negligible disparities between them, indicating strong correlations between the predicted and reference values.

Potatoes are one of the widely consumed staple foods all over the world. The prices of potatoes were more unstable than other agricultural commodities because of factors such as perishability, production uncertainties, and seasonal fluctuations. These factors make it difficult for farmers to manage and predict production levels, resulting in supply and price fluctuations. Therefore, it is essential to develop predictive models that can accurately forecast the pricing of agricultural commodities such as potatoes. The study attempted to explore the pattern of potato prices in major markets of northern India using different time series models. The empirical findings indicated positively skewed data distributed with a high instability index. In terms of forecasting accuracy, the EEMD-ANN model exhibited the best performance among the various time series techniques, generating the lowest MAPE values of 9.10%, 12.97%, and 4.27% for the Chandigarh, Delhi, and Shimla markets, respectively. Meanwhile, the EEMD-ARIMA model yielded the most accurate prediction results for the Dehradun market, with an MAPE value of 12.97%. The outcomes of this study offer significant insights to farmers, consumers, and government bodies for making informed decisions regarding the production, consumption, and distribution of potatoes. Moreover, the effectiveness of various time series models in handling complex agricultural price series was also investigated.

The type of oil used during deep-fat frying can play a unique role in the transfer phenomena due to their different physicochemical properties. The effects of three types of oil, including canola, sunflower, and soybean oils, on the momentum, heat, and mass transfer during frying of potato strips were evaluated. The results showed that the oil type did not have a significant effect on moisture loss and oil uptake. However, the velocity distribution patterns of the three oils were not the same. The average simulated velocity for soybean oil was higher than those of the other two, attributable to its higher density. The results showed that the surface temperature of potatoes was affected by oil type. Overall, the developed numerical simulation could help in a better comprehension of the deep-fat frying of potato strips with the ultimate aim of producing low-fat quality products.