Potato Research

Abstract The aim of this study is to analyse potato cultivation in South Asian Association of Regional Cooperation (SAARC) countries from 1961 to 2022, based entirely on secondary data from the Food and Agriculture Organization. By employing the ARIMA model, the research forecasts potato area and production up to 2030, with ARIMA (1, 1, 5) identified as the optimal model for both area and production in Afghanistan, Bangladesh, Sri Lanka, India, Myanmar, Nepal, Pakistan and China with a 95% accuracy level. By the year 2030, the projected potato area and production are expected to be 69,514.75 ha and 937,406.30 t in Afghanistan, 473,612.08 ha and 10,561,509.80 t in Bangladesh, 6,224,031.90 ha and 107,944,218.99 t in China, 2,447,779.92 ha and 61,310,173.10 t in India, 29,198.17 ha and 447,014.54 t in Myanmar, 220,857.06 ha and 3,885,372.21 t in Nepal, 464,614.77 ha and 10,154,642.65 t in Pakistan, and 4720.31 ha and 78,391.00 t in Sri Lanka. The trend analysis reveals non-linear patterns, with quadratic, exponential, and cubic trends standing out as the most suitable for depicting the series’ behaviour. The examination of instability levels showcases varying trends, with some countries experiencing a decrease while others show an increase. To ensure the long-term sustainability of potato cultivation, targeted strategies focusing on enhancing access to quality inputs, promoting efficient farming practices, and addressing volatility factors like market fluctuations and pest outbreaks are crucial. The study emphasizes the significance of monitoring and mitigating risks associated with potato cultivation to ensure stable and sustainable production. Sustainability is evaluated through the Sustainability Index, employing three methods, with the study highlighting the importance of maintaining productivity over an extended period. By providing insights into historical trends, volatility, and sustainability, this research offers a roadmap for well-informed judgement and calculated planning in the field of potato farming, ultimately contributing to food security and economic development in the SAARC region.

Abstract Estimating leaf nutrient concentration in field crops is essential to increase crop yield by optimum fertiliser application. Notably, these practices become more critical for short-cycle crops like potatoes (Solanum tuberosum L.), where conventionally, laborious in-field plant sampling and laboratory analysis take a long time. Multiple samples are frequently required to reach the field’s representation and reliability. The alternative technique of optical spectroscopy, which reports the canopy reflectance to the specific band of the electromagnetic spectrum, can be used to estimate the plant nutrient concentration. Previous studies have made such efforts using the electromagnetic spectrum’s visible to near-infrared (VNIR, 400–1100 nm) and short-wave infrared (SWIR, 1100–2400 nm) ranges. In this study, we are testing the ability of the spectroscopy with a full-range spectroradiometer (400–2400 nm) along with a comparison of VNIR and SWIR to estimate the total Kjeldahl nitrogen (TKN), phosphorus (P), potassium (K), and sulphur (S) nutrient concentration in freshly picked petiole/leaf samples of potato plants. Results show that the full-range spectrum predicted TKN with an accuracy of R2 = 0.91 external validation (0.74 internal validation), followed by K, R2 = 0.87 (0.69), P, R2 = 0.86 (0.82), and S with R2 = 0.75 (0.68). It was also reported that the maximum difference in the estimation accuracy among VNIR and SWIR was reported for K, where VNIR had R2 = 0.48 (0.54) and SWIR had R2 = 0.86 (0.80). This study lays a foundation for further development of models that can estimate the canopy nutrient concentration in the field with spectral reflectance and scale up these models with hyperspectral imaging.

Abstract The diseases that particularly affect potato leaves are early blight and the late blight, and they are dangerous as they reduce yield and quality of the potatoes. In this paper, different machine learning (ML) models for predicting these diseases are analysed based on a detailed database of more than 4000 records of weather conditions. Some of the critical factors that have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, and atmospheric pressure. These types of data relationships were comprehensively identified through sophisticated means of analysis such as K-means clustering, PCA, and copula analysis. To achieve this, several machine learning models were used in the study: logistic regression, gradient boosting, multilayer perceptron (MLP), and support vector machine (SVM), as well as K-nearest neighbor (KNN) models both with and without feature selection. Feature selection methods such as the binary Greylag Goose Optimization (bGGO) were applied to improve the predictive performance of the models by identifying feature sets pertinent to the models. Results demonstrated that the MLP model, with feature selection, achieved an accuracy of 98.3%, underscoring the critical role of feature selection in improving model performance. These findings highlight the importance of optimized ML models in proactive agricultural disease management, aiming to minimize crop loss and promote sustainable farming practices.

Abstract This study explores soil and fertilizer management techniques using winter cereal rye and Chlorella sorokiniana microalgae biofertilization alongside mineral and organic fertilizers for spring–summer potato cultivation in both conventional (CONV) and organic (ORG) production systems in subtropical environments. Traditional soil management, with a fallow period followed by subsoiling, plowing, and harrowing, served as the reference standard for comparisons with four alternative methods in CONV and ORG systems. In the CONV system, cereal rye plants were terminated with glyphosate and the alternative soil managements included (i) incorporating chopped cereal rye with standard soil tillage, (ii) no-till planting into chopped cereal rye, (iii) planting into chopped cereal rye after soil chiseling, and (iv) mulching chopped cereal rye residues on the ridges of potato planted after standard soil tillage. In the ORG system, the alternatives included (v) incorporating fresh cereal rye with standard soil tillage, (vi) no-till planting into standing fresh cereal rye plants, (vii) no-till planting into cereal rye terminated with a knife roller, and (viii) mulching whole cereal rye plants between the ridges of potato planted after standard soil tillage. Each soil management was combined with treatments of no fertilization or either mineral or organic fertilization with or without microalgae application. Amid severe water constraints, particularly due to La Niña events, standard soil tillage in CONV and no-tillage in ORG both on cereal rye crops respectively increased (39.5%) total tuber yield and number of tubers per plant (18.8%), showing themselves as potential conservation soil managements to potato crop. Microalgae with respective fertilizer application exclusively associated with chopped cereal rye residues on hills in CONV and with no-till planted into fresh plants of cereal rye in ORG favored tuber filling.