Potato yield forecast by using guttation test method in household laboratory conditions

Assessment of a water filtration device for household use in rural communities in Mexico

Assessment of a water filtration device for household use in rural communities in Mexico

A reliable, pre-harvest, crop yield prediction based on meteorological factors is important to anticipate adverse effect of weather variables. Discriminant score-based regression models, MLP artificial neural network (ANN) models, and regression-ANN hybrid models were used to model potato (Solanum tuberosum L.) yield. Maximum and minimum temperatures, rainfall, and relative humidity, and their indices, were used to obtain discriminant scores for each year. These discriminant scores, along with a time variable, were used as inputs and potato yield as outputs for the development of models. A hybrid model consisting of linear and non-linear components performed better than individual models if combined linearity and nonlinearity are present in the data, else the ANN models were better than regression models. The best models can be used to obtain a reliable forecast of potato yield at 6–8 weeks before harvest using meteorological data.

Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.

The objectives were to evaluate the properties of refined (ROO) and extra-virgin olive oil (EVOO) in their natural state (fresh) and after heating, while comparing them with each other and with refined soybean (SBO) and refined sunflower seed oil (SFO). The methodology was designed to simulate, in controlled laboratory conditions, the home-frying process, while evaluating fatty acid profile (fatty acid methyl esters were separated by gas chromatography), concentration of phenolic compounds (Gallic acid dosage), antioxidant activity (DPPH), and production of polar compounds (thin layer chromatography) before and after heating to 200 oC for six minutes. It was observed that, before and after heating, SBO and SFO are rich in polyunsaturated fatty acids (FA) and ROO and EVOO are rich in monounsaturated FA. Fresh or heated, ROO and EVOO do not have trans FA, which are present in SBO and SFO, and increase in SBO after heating (+ 32.8%). The concentrations of phenolic compounds are always higher in olive oils, despite the decrease that occurs after heating (-7.5% in the ROO and -24.6% in EVOO). Antioxidant activity is greater when olive oils are fresh and remains present in EVOO after heating. The concentration of polar compounds was similar for all oils after heating. In conclusion, ROO and EVOO are the richest in monounsaturated FA even after heating, with no production of saturated or trans FA. Despite losing some antioxidant activity, heated EVOO remains richer in monounsaturated FA than ROO, SBO and SFO in the fresh version. All oils suffer similar rates of degradation.

<p>The objectives were to evaluate the properties of refined (ROO) and extra-virgin olive oil (EVOO) in their natural state (fresh) and after heating, while comparing them with each other and with refined soybean (SBO) and refined sunflower seed oil (SFO). The methodology was designed to simulate, in controlled laboratory conditions, the home-frying process, while evaluating fatty acid profile (fatty acid methyl esters were separated by gas chromatography), concentration of phenolic compounds (Gallic acid dosage), antioxidant activity (DPPH), and production of polar compounds (thin layer chromatography) before and after heating to 200 °C for six minutes. It was observed that, before and after heating, SBO and SFO are rich in polyunsaturated fatty acids (FA) and ROO and EVOO are rich in monounsaturated FA. Fresh or heated, ROO and EVOO do not have trans FA, which are present in SBO and SFO, and increase in SBO after heating (+ 32.8%). The concentrations of phenolic compounds are always higher in olive oils, despite the decrease that occurs after heating (-7.5% in the ROO and -24.6% in EVOO). Antioxidant activity is greater when olive oils are fresh and remains present in EVOO after heating. The concentration of polar compounds was similar for all oils after heating. In conclusion, ROO and EVOO are the richest in monounsaturated FA even after heating, with no production of saturated or trans FA. Despite losing some antioxidant activity, heated EVOO remains richer in monounsaturated FA than ROO, SBO and SFO in the fresh version. All oils suffer similar rates of degradation.</p>

Resource dependency of food production is aggravated when food is wasted. In Sweden, it is estimated that 37% of the total bread waste is generated at the household level. This work aimed to assess whether fermentation using edible filamentous fungi at households can provide a solution to valorize leftover bread in the production of fungi-based food for consumption. Bread was fermented in household and laboratory conditions with Neurospora intermedia and Rhizopus oligosporus. The results show that bread can be successfully and easily fermented at households, without signs of microbial contamination even though the conditions were not sterile. Fermentation at the household resulted in higher protein, fat and fiber content as well as greater starch reduction compared to the samples fermented under laboratory conditions. Household engagement in bread fermentation will likely depend on values that motivate reusing leftover bread. Perceived values that are expected to motivate engagement vary across individuals, but may include improved nutritional benefits, food waste prevention, convenience, responsibilities, and being part of sustainable societies and actions.

Crop growth monitoring and its yield forecasting is a pre-requisite task to ensure food security and to assess the economic return from agriculture. The application of remote sensing techniques in agricultural sector enhanced the potentiality of crop growth monitoring and estimation of crop yields. Over the last few years, potato cultivation in Bangladesh has promisingly increased. On the contrary, proper distribution capability and the storage capacity is still under the desirable condition due to lack of management system. In this context, remote sensing image analysis techniques were employed in this study to predict potato yield before harvesting to improve management system. This study used 16-day high resolution (~ 30 m) Landsat surface reflectance data for the year of 2010–2011 to detect the maximum normalized difference vegetation index (NDVI) value of a potato growing season. The NDVI will be the maximum after the 63 days of the plantation for Munshiganj District of Bangladesh. Afterward, six satellite images were selected from six growing seasons: 2010–2011, 2011–2012, 2012–2013, 2013–2014, 2014–2015, and 2015–2016 based on the day of the maximum NDVI. A total of 35 potato fields were identified from the filed investigation over the study area to extract the NDVI values from satellite images. Regression analysis was performed between NDVI values and filed level potato yields. The maximum coefficient of correlation (R2) of yield prediction equation was found to be 0.81 between the mean NDVI and potato yield. The equation was validated by using data from 2016 to 2017 growing season, and the result revealed that the difference between predicted and actual filed yield is about 10.4%. It has been found that the high-resolution Landsat images can be an effective means for early estimation of potato yield.

Deterministic potato (Solanum tuberosum L.) growth models hardly rely on driving seasonal field variables that directly characterize spatial variation of plant growth. For example, the SUBSTOR model computes the leaf area index (LAI) as an auxiliary variable from meteorological conditions and soil properties. Empirical models may account for seasonal LAI functions and accurately predict potato yield. The objective was to evaluate multiple linear regression (MLR) and neural networks (NN) as predictive models of potato yield. Using data from several replicated on-farm experiments conducted over 3 years, model performance was evaluated for their capacity to forecast tuber yields 9, 10 and 11 weeks before harvest compared to SUBSTOR. A 3-input NN using LAI functions and cumulative rainfall yielded the most accurate estimations and forecasts of tuber yields. This NN showed that tuber yield of contrasting zones was mostly a function of meteorological conditions prevailing during the first 5–8 weeks after planting. Subsequent development of tubers was essentially controlled by biomass allocation to tubers. The NN models were more coherent than MLR and SUBSTOR for two reasons: (1) the use of seasonal LAI directly as input rather than computed as an auxiliary variable and (2) the non-linearity of the modeling process resulting in more accurate estimation of the temporal discontinuities of potato tuber growth. This model showed potential for application in precision agriculture by accounting for temporal and spatial real-time climatic and crop data.

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.

This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating diverse data sources, including meteorological, phenotypic, and remote sensing data. Advances in computer technology have enabled the creation of more sophisticated models, such as mixed, geostatistical, and Bayesian models. Special attention is given to deep learning techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex data patterns. The article also discusses the effectiveness of other algorithms, such as Random Forest and Support Vector Machines, in capturing nonlinear relationships affecting yields. According to standards adopted in agricultural research, the Mean Absolute Percentage Error (MAPE) in the implementation of prediction issues should generally not exceed 15%. Contemporary research indicates that, through the use of advanced and accurate algorithms, the value of this error can reach levels of even less than 10 per cent, significantly increasing the efficiency of yield forecasting. Key challenges in the field include climatic variability and difficulties in obtaining accurate data on soil properties and agronomic practices. Despite these challenges, technological advancements present new opportunities for more accurate forecasting. Future research should focus on leveraging Internet of Things (IoT) technology for real-time data collection and analyzing the impact of biological variables on yield. An interdisciplinary approach, integrating insights from ecology and meteorology, is recommended to develop innovative predictive models. The exploration of machine learning methods has the potential to advance knowledge in potato yield forecasting and support sustainable agricultural practices.

Forecast of tuber yield by haulm growth, if possible, may mean a lot for the potato breeding and cultivation. Since it has been said that there is no high correlation between haulm growth and tuber yield in potatoes, we have tried to classify ecological types according to the different processes of haulm growth, thus studying relations between ecological type and tuber yield. The stem length from the first peduncle of main stem to soil surface was named the stem length under peduncle, and that from the first peduncle to the growing point the stem length above peduncle. The stem length under peduncle was revealed to have a close connection with haulm growth from early to middle stage of growing season, and the stem length above peduncle with that from middle to late stage. In many cases high positive correlations were found between the stem length under peduncle and starch yield from tubers, while relatively high negative or low positive correlations were found between the stem length above peduncle and starch yield from tubers. Haulm growth, showing longer stem length above the peduncle, especially provided with shorter stem length under the peduncle, resulted delayed growth, excessive foliage growth and lodging. Consequently growing conditions were unfavolable for tuber bulking and accumulation of starch in tubers. By means of laies the stem length under the peduncle on the X axis and the stem length above the peduncle on the Y axis, dividing both by their mean values, were obtained four ecological types. From the table 5, it was known that type I and IV, accompanied by longer stem length under the peduncle produced more starch yield than type II and III that have shorter stem length under the peduncle. Comparing type II with IV, although each type had nearly equal stem length, features of the growth are different and accumulation of starch in tubers was greater in the type IV than in the type II. In conclusion, it was suggested that studies of cultivation method accelerate the stem length under the peduncle must be done and in breeding potatoes, elimination of such type as II would be desirable.

The object of this study was the technology of precision agriculture and remote monitoring of weather conditions. The task to evaluate the effectiveness of using precision farming and precision irrigation technologies in the activities of agricultural enterprises under different conditions, in particular, different climatic and weather conditions, has been considered. To solve the task, a hardware-software system for smart irrigation and remote monitoring of weather conditions in the activities of agricultural enterprises during the cultivation of agricultural crops was designed and described. Results of the system's performance were analyzed in the activities of the Ukrainian agricultural company, which grew potatoes of various varieties in the Kyiv oblast (Ukraine) from 2021 to 2023. The results show that the average yield of potatoes of different varieties without irrigation for three years of observation was 29.74 t/ha, with irrigation – 48.99 t/ha, and with smart irrigation – 55.26 t/ha. At the same time, in the latter case, water, human, and financial resources were saved. The increase in yield with smart irrigation compared to yield with conventional irrigation over the three years of observation was on average 12.8 %. According to the results of the implementation of the hardware-software system for smart irrigation and remote monitoring of weather conditions in Ukraine, the effect of the possible implementation of this system by agricultural companies in the Republic of Kazakhstan was analyzed. The forecast of the average yield of potatoes for the period from 2024 to 2026 was built based on the model of linear weighted moving average, taking into account corrections in the case of using smart irrigation. Data on potato yield from 1990 to 2023 were chosen as the basis. The use of smart irrigation according to the described technology could increase the yield of potatoes of various varieties on average from 31.71 t/ha to 35.78 t/ha in comparison with the forecast values of yield without irrigation at the level of 19.25 t/ha. This confirms the need to apply the transfer of precision farming technologies to increase the yield of agricultural crops, in particular potatoes, and the productivity of agricultural companies

Simple SummaryThe tomato bug is a generalist predator commonly used to control insect pests. This exotic mirid was found in 2012 in South Texas and has been established in this region. It was initially observed feeding on nymphs of the potato psyllid in tomato crops. The potato psyllid is the vector of the fastidious bacterium that causes disease in various night-shade crops, including potato zebra chip disease (ZC), with economic losses that by the mid-2000s escalated to tens of millions of dollars in the United States, Mexico, and Central America. We assessed interactions between tomato bugs and potato psyllids in three different environmental settings. First, we estimated the numeric response of tomato bugs preying on potato psyllids under laboratory and greenhouse conditions. Second, we evaluated the predator–prey interaction under controlled field cage conditions. Third, we exposed tomato bugs under controlled field release conditions to the natural occurrence of potato psyllids under a reduced insecticide program. Finally, we assessed its impact on ZC disease incidence, severity in potato tubers, and potato yield. In laboratory and greenhouse experiments, tomato bug response preying resulted in the potentially beneficial effects of the predacious tomato bug reducing potato psyllid populations. Overall, the controlled release of tomato bugs under field conditions significantly reduced potato psyllid incidence in potatoes. Furthermore, the combination of tomato bugs with a reduced insecticide program increased potato yields, but only reduced ZC tuber incidence in one of the two potato cultivars evaluated in one season. Findings from these studies indicate that tomato bugs could be effective as a biological control agent for potato psyllids in potato production.Nesidiocoris tenuis (Hemiptera: Miridae) is a generalist predator commonly used to control the whitefly Bemisia tabaci in Europe. This mirid has been found and established in South Texas, where it was initially observed feeding on nymphs of the psyllid Bactericera cockerelli (Hemiptera: Triozidae) in open tomato fields. B. cockerelli is the vector of the fastidious bacterium “Candidatus Liberibacter solanacearum” that causes diseases in several solanaceous crops, including zebra chip (ZC) disease in potatoes. There is a need to better understand how this predator impacts the control of important crop pests, such as potato psyllids. We assessed the interactions between N. tenuis and B. cockerelli in three different environmental settings. First, we estimated the numeric response of N. tenuis preying on B. cockerelli under laboratory and greenhouse conditions. Second, we evaluated the predator–prey interaction under controlled field cage conditions. Then, we exposed N. tenuis under controlled field release conditions to the natural occurrence of B. cockerelli. Finally, we assessed the compatibility between the use of N. tenuis as a biological control agent in a field study and its impact on ZC disease incidence, severity in potato tubers, and potato yield. Laboratory and greenhouse experiments resulted in diverse types of functional model responses, including exponential and linear mathematical models. Our findings revealed a significant predation effect exerted by N. tenuis, resulting in a reduction of more than fourfold in the number of B. cockerelli nymphs per cage. Specifically, the nymphal population decreased from 21 ± 3.2 in the absence of N. tenuis to 5 ± 1.6 when N. tenuis was present. Furthermore, the combination of N. tenuis with a reduced insecticide program increased potato yields, but only reduced ZC tuber incidence in one of two potato cultivars evaluated, and in one season. Findings from these studies indicate that N. tenuis could be effective as a biological control agent for B. cockerelli in potato production in South Texas. This is the first report of N. tenuis preying on immature stages of any psyllid species.

The paradigm shift toward ecological agriculture has spurred efforts to phase out the use of conventional pesticides, and researchers are actively seeking natural alternatives to replace these chemicals. Aim: This study aimed to introduce an innovative intervention to enhance potato yields in a non-invasive manner, thereby elevating the content of both dry matter and starch. Ultimately, this approach seeks to enhance the quality of raw materials destined for the production of potato-enriched products. A field experiment was conducted in central-eastern Poland that employed a randomized sub-block method within a dependent split-split-plot design replicated three times. The main factor was pre-planting treatments, which included the application of a microbiological preparation to seed potatoes for: (a) 10 min, (b) 15 min, and (c) no treatment (control). Another factor was the fourteen potato cultivars of different earliness groups. Qualitative analyses were carried out in laboratory conditions. The application of the microbiological preparation yielded positive outcomes on the physiological indicators of potato yield, while augmenting the production of dry matter and starch when compared to the control object. The analyzed cultivars had the most pronounced impact on both the content and yield of dry matter and starch, demonstrating a wide array of responses to pre-planting treatments that incorporated microbiological preparations. For the first time in studies concerning effective microorganisms, chlorophyll fluorescence analysis was employed. Alternative solutions in the form of employing microbiological preparations offer the potential to effectively substitute pesticides and synthetic fertilizers in potato production, consequently enhancing the quality of raw materials for food.