Potato Research最新文献

The current rapid increase in ambient carbon dioxide concentration ([CO₂]) and global temperatures have major impacts on the growth and yield of crops. Potato is classified as a heat-sensitive temperate crop and its growth and yield are expected to be negatively affected by rising temperatures, but it is also expected to respond positively to increasing ambient [CO₂]. In this study, we investigated the physiological, growth, and yield responses of two potato cultivars to elevated temperature (eT) and the possible role of elevated [CO₂] (e[CO₂]) in counteracting the negative effects of eT. Two growth chamber trials (trials 1 and 2) were conducted using two temperature regimes: ambient temperature (aT, T_min/T_max = 12/25 ℃) and eT (T_min/T_max = 15/38 ℃), and two [CO₂]: ambient (a[CO₂]) = 415 ppm and e[CO₂] = 700 ppm. Temperatures gradually rose from the minimum at 6.00 AM to reach T_max at noon, then T_max was maintained for 1 h in trial 1 and for 4 h in trial 2. Elevated [CO₂] increased photosynthesis (Anet) in both cultivars at aT and eT. Elevated temperature also stimulated Anet compared to aT. Elevated [CO₂] significantly reduced stomatal opening size, while eT resulted in larger stomata openings and higher stomatal conductance. Elevated [CO₂] increased tuber yields at aT in both trials. Tuberisation was delayed by eT in trial 1, and completely inhibited in trial 2 even at e[CO₂], resulting in no tuber yield. The two cultivars responded similarly to treatments, but Mondial initiated more tubers and had higher tuber yield than BP1. The results suggest that potato will benefit from e[CO₂] in future, even when exposed to high T_max for a short period of the day, but the benefit will be eroded when the crop is exposed to high T_max for an extended period of the day.

Potassium is an essential nutrient element for potato production. However, there is little research on how the base/topdressing ratio of potassium fertilizer affects plant growth. Therefore, in this 2-year (2022–2023) study, we used Longshu 7 as the experimental material and conducted a pot experiment. Under the condition of total potassium application of 5.4 g/plant, the potassium fertilizer base/topdressing ratios were as follows: CK (10:0), T1 (2:8), T2 (4:6), T3 (6:4), and T4 (8:2). We investigated the effects of potassium fertilizer application on dry matter quality, endogenous hormones, photosynthetic fluorescence characteristics, carbon and nitrogen metabolism and yield in potato. The results of the study demonstrated that potassium topdressing had a positive effect on plant growth through the optimization of endogenous hormone content and regulation of cell elongation. In addition, potassium application can enhance the activity of enzymes related to carbon and nitrogen metabolism, promote photosynthesis, improve the transport efficiency of photosynthetic products and enhance the dry matter quality of tubers. Among all the potassium topdressing treatments, the T2 treatment exhibited a significant difference. However, it is important to note that an excessive increase in the base/topdressing ratio of potassium fertilizer may have detrimental effects on the levels of gibberellin A3 (GA₃) and starch content. Based on Pearson correlation analysis, it was determined that the activities of sucrose synthase (SuSy), sucrose phosphate synthase (SPS) and glutamine synthetase (GS) play a significant role in influencing the dry matter quality of potato tubers. These findings provide valuable insights into the importance of these factors in potato production. Overall, the results of this study highlight the significance of maintaining an appropriate ratio of base to topdressing of potassium fertilizer. This optimal ratio ensures the efficient assimilation and utilization of nitrogen and carbon, ultimately serving as a valuable theoretical foundation for effective potassium fertilizer application in potato production.

Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an R² of 0.49168, while XGBoost had an MSE of 0.03583 and an R² of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an R² of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.

Potato (Solanum tuberosum L.) is one of our important agricultural products, which is the main food source for people in Türkiye, as well as all over the world. There are many diseases and pests that reduce productivity in potato plant production. Potato cyst nematodes (Tylenchida: Heteroderidae) are pests that are on the quarantine list of the European and Mediterranean Plant Protection Organization and cause serious yield losses. Since they are soil-borne pathogens and there is no effective chemical control, the most successful control method is to use resistant cultivars. The aim of the study was to determine the resistance levels of local and national potato cultivars and clones developed by the Nigde Potato Research Institute against the Globodera rostochiensis Ro2/3 pathotype using molecular marker analysis and biotesting methods. The biotest study was carried out by inoculating 7500 eggs and larvae of the Globedera rostochiensis pathotype Ro2/3 into pots. In the molecular marker analysis, resistance was investigated with TG689, 57R, Gro1-4 markers. While all cultivars and clones except Bettina were grouped as sensitive in the biotesting study, the H1 resistance gene was detected in Onaran, Ünlenen, Leventbey, Muratbey, Nahita, Agria, Madeleine, Desiree and Bettina cultivars by molecular marker analysis. H1 and Gro1-4 resistance genes were detected in the PAE 13–08-07, PAE 13–08-08 and PAE 13–08-14 clones used in the experiment. The results showed that clones developed by the Potato Research Institute exhibited highly resistant marker alleles for the Ro2/3 pathotype of G. rostochiensis. The results of phenotyping study and the molecular marker study were not similar.

Despite recent advances in the prevention and control of nutritional deficiencies, estimates suggest that over two billion individuals worldwide are at risk for vitamin A, iodine and/or iron insufficiency. Pregnant women and small children are most at risk, and Southeast Asia and sub-Saharan Africa have very high incidence rates. Concerning public health are deficits in zinc, folate and the B vitamins, among other micronutrients. Micronutrient malnutrition, often referred to as hidden hunger, represents one of humanity’s most pressing challenges. Iron deficiency anaemia affects more individuals globally than any other prevalent disorder. However, iron supplementation can exacerbate infectious diseases, necessitating careful evaluation of iron therapy policies. In this review, we explore biofortification strategies to combat hidden hunger, considering recent medical and nutritional advancements. Enhancing iron content in edible plant parts can improve human nutrient status through crop consumption. Mineral and vitamin density in staple foods, particularly for impoverished populations, can be increased using traditional plant breeding or transgenic approaches, collectively known as biofortification. Microbial iron biofortification is especially valuable in developing countries where expensive supplements are unaffordable. Additionally, the current COVID-19 pandemic underscores the need for a robust immune system, with iron playing a crucial role in immune function enhancement.

Potato diseases pose a significant threat to farmers, impacting potato crops’ productivity, quality, and financial stability. Among the most notorious diseases is late blight, caused by Phytophthora infestans, famously responsible for triggering the Irish Potato Famine in the 1840s. Late blight swiftly devastates potato foliage and tubers, particularly in damp, humid conditions. Another common disease is early blight, attributed to Alternaria solani. This disease affects various parts of the potato plant—leaves, stems, and tubers. It mainly shows up in the form of dark stains around the center of a bull’s eye on the leaves, bringing down both the yield and the crop quality. A model consisting of a Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) enhanced for potato disease detection was proposed in our paper. The dataset used was Z-score standardized before the training and testing process using the proposed CNN-LSTM model was started. The performance of the implemented model, CNN-LSTM, was analyzed alongside five traditional machine learning algorithms, namely Random Forest (RF), Extra Trees (ET), K-Nearest Neighbours (KNN), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM). Accuracy, sensitivity, specificity, F-score, and AUC were the metrics included in the evaluation, confirming the effectiveness of the models. The results of the experiments showed that our CNN-LSTM reached the highest accuracy at 97.1%.

The persistence of agrarian crises in Punjab state of India has necessitated the policymakers to identify new institutional agri-businesses to make farm growth sustainable and inclusive. Contract farming in high-value crops such as potato is seen as one of the several ways to develop new market linkages with the farmers and improve farming income consistently through the dissemination of new production and post-harvest processing technologies. In this context, a comparative analysis of contract farmers associated with the PepsiCo company and non-contract farmers growing potato for local markets is carried out in Punjab to determine the empirical gains that accrue to contract farmers and the role of contract farming in farm diversification. The findings revealed that the company procures potatoes at farmers' fields through quad-partite and written contractual agreements, extends extension and training facilities at the farmers' doorstep, and provides yield-based incentives to the farmers. Contract farmers earned more profits than their counterpart non-contract farmers mainly due to their better price realization, although contract farmers had lower yields and higher cost of production than non-contract farmers. However, the imposition of the condition of growing at least 4 ha of area under potatoes led to the exclusion of small farmers and did not lead to diversification away from traditional crops to high-value crops such as potato. The study argues that the Punjab government needs to play a proactive role in facilitating the participation of small farmers through group contracts and Farmer Producer Organizations (FPOs) to enhance farmers' income and crop diversification through potato contract farming.

As an important food crop in the world, potato has been attracting scholarly attention to improve its yield in the future, particularly under climate change. Therefore, analyzing the potential yield of potato as affected by global warming is of great significance to direct the production of crops worldwide. However, up to now, most research reports estimated the potential yield of potatoes by models which are based on the theory of production functions while there are few theoretical studies on the time-series approach based on stationary stochastic processes. Thus, in this paper, both average and top (national) yields of potato between 2021 and 2030 are projected creatively using an auto-regressive integrated moving average and trend regression (ARIMA-TR) model basing the projection on historic yields from 1961 to 2020 to explore the potential yield of the crop in the future; the effects of global warming on both average and top (national) yields of potato from 1961 to 2020 are analyzed using binary regression models in which global mean temperature is treated as the independent variable and the yield as the dependent variable, to reveal how climatic events drive the variation trend of these two types of yield. Our results show that between 2021 and 2030, the average yield of potato is projected to be from 21,234 to 23,773 kg/ha while the top yield ranges from 50,240 to 51,452 kg/ha; the average will approach from 42.26 to 46.20% of the top, or the gap between these two yields will be gradually narrowed in the ensuing decade; from 1961 to 2020, global warming exerts a positive effect on the average yield of potato with a quadratic function (R-squared = 0.772 and F = 96.417) more than on the top yield with an inverse function (R-squared = 0.568 and F = 76.201), which partly makes the gap between these two types of yields shrink. Our study concludes that for potato by 2030, the opportunities for improving global production should be dependent on both high- and low-yield countries as the average yield is in the main body of an S-shaped curve in the evolutionary trend in the long run. These insights provide the academic circle with innovative comprehension of the potential yield of potato for global food security under climate change.

The antibacterial activity of essential oils (EOs) from Carum carvi, Cinnamomum zeylanicum, Cuminum cyminum, Eugenia caryophyllus, Foeniculum vulgare, Melaleuca alternifolia, Mentha × piperita, Origanum vulgare, Rosmarinus officinalis and Thymus vulgaris was tested against Pectobacterium carotovorum subsp. carotovorum (Pcc) and Pectobacterium atrosepticum (Pa), which cause soft rot of potato tubers. In disc diffusion, minimum inhibitory concentration and minimum bactericidal concentration (MBC) tests, cinnamon EO was found to be most effective against both bacteria. The inhibition zones ranged from 20.46 to 29.58 mm for a concentration of 100 μL/mL. The minimum inhibitory concentration was 0.5 μL/mL, and MBC was between 0.5 and 5 μL/mL. The higher sensitivity of bacteria was manifested in clove (Pcc and Pa), mint (Pcc), oregano (Pa) and thyme (Pa) EOs. Rosemary EO was the least effective. The results of the in vivo test were not entirely consistent with those of the in vitro tests. The most significant antibacterial effect was achieved with mint EO. The treatment of potato tuber discs with mint EO at a concentration of 3 μL/mL for Pcc and 3–10 μL/mL for Pa was 100% effective. The efficacy of the essential oils of caraway (5–10 μL/mL), thyme (10 μL/mL) and oregano (5 μL/mL) also ranged from 95.7 to 99.7%. Based on the results of the in vivo test, it may be recommended that mint EO and potentially caraway, oregano and thyme EOs be further tested for pickling potato tubers against bacteria of the genus Pectobacterium.

Regarding the potato market, pricing fluctuations are a significant factor, and unfortunately, they cause many issues for producers and consumers. It happens to result in food insecurity and economic instability. This study brings in an advanced LSTM-RNN model built to predict potato prices, which might alleviate the mentioned challenges. We gathered a historical potato price database and other economic variables, normalized by the Z-score normalization method to ensure all the data was consistent and credible. The model’s effectiveness was benchmarked against five traditional machine learning models: we used K-nearest neighbor, random forest, support vector regressor, linear regression, and gradient boosting regressor to classify isolated households and determine their socioeconomic status. The empirical data implied that our proposed LSTM-RNN model was more efficient than all comparison models, leading to an R² value of 0.98. The paper not only substantiates the plausibility of applying deep learning to address the agricultural market prediction issue but also serves as a guideline noting the capabilities of the LSTM-RNN routine in improving the decision-making processes for the farmers participating in the sector. This model supports a sustainable food system and a balanced economy by bringing price stability integral to designing and implementing strategies to address food security.