Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island

Afzaal, Hassan; Farooque, Aitazaz A.; Abbas, Farhat; Acharya, Bishnu; Esau, Travis

doi:10.3390/su12062419

Cited by 48 publications

(46 citation statements)

References 25 publications

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“…fields in New Brunswick and Prince Edward Island [33], while the slope and elevation of agricultural fields explained 22-36% of variation in the tuber yield of these regions [34]. Afzaal et al [35] reported that supplemental irrigation and irrigation application techniques varyingly affected the tuber yield especially in rainfed areas due to uneven rainfall patterns. Potato tuber yield also depends on the tuber seed quality, soil management practices, nitrate contents in the soil, fertilizer, water management practices, and chemical and bio-fertilization [36][37][38][39].…”

Section: Comparative Analysis Of Machine Learning Algorithmsmentioning

confidence: 99%

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

et al. 2020

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Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.

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Section: Comparative Analysis Of Machine Learning Algorithmsmentioning

confidence: 99%

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

et al. 2020

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“…It has been reported that rainfed potato crop management may shift to irrigation, a necessary condition to reach a yield above the potential in the Quebec region (Parent and Anctil, 2012). In addition, climate changes may increase the use of irrigation to avoid periods of drought due to irregularity and poor distribution of precipitation (Afzaal et al, 2020). Thus, irrigation practices will inevitably be more common, which subsequently increase the pressure on local water resources since only 10% can be reused (Shaffer and Runkle, 2007).…”

Section: Introductionmentioning

confidence: 99%

Impacts of Water Stress Severity and Duration on Potato Photosynthetic Activity and Yields

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“…Although the farmers cannot control rain, proper irrigation scheduling can prevent the leaching of unused plant nutrients in case of continuous drought periods. Changing patterns of rain caused by climate change would bring possible drought, necessitating irrigation of crops for parts of the world that are rain-fed and do not currently need supplemental irrigation [59].…”

Section: Effect Of Rate Of Amendment Applicationsmentioning

confidence: 99%

Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates

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The knowledge about nutrient dynamics in the soil is pivotal for sustainable agriculture. A comprehensive research trial can retort unanswered questions. Dynamics of nutrients sourced from organic amendment types (chicken manure, dairy manure, and MilorganiteTM) applied at different rates (0, 168, 336, 672 kg total N/ha) were monitored within and below the rootzone of collard greens cultivated on a sandy loam soil in Prairie View, TX, USA. Macro- and micronutrients (e.g., TN: total nitrogen, P: phosphorous, K: potassium, Na: sodium, Ca: calcium, Mg: magnesium, B: boron, Cu: copper, Fe: iron, and Zn: zinc) were analyzed from soil solution samples collected during six sampling periods from within and below the rootzone. As hypothesized, the organic amendment types and rates significantly (p < 0.05 and/or 0.01) affected nutrient dynamics within and below the crop rootzone. Chicken manure released significantly more TN, P, K, Na, Ca, Mg, B, Cu, and Fe than the other two amendments. The application of chicken manure and MilorganiteTM resulted in higher below-the-rootzone leachate concentration of TN, Na, Mg, and Ca than in the leachates of dairy manure. Dairy manure treatments had the lowest concentrations of TN, Ca, and Mg; whereas, MilorganiteTM had the lowest concentrations of P, K, Na, B, and Cu in the collected leachates. The higher level of P (i.e., 4% in MilorganiteTM as compared to 2 and 0.5% in chicken and dairy manures, respectively, might have reduced the formation of Vesicular-Arbuscular (VA) mycorrhizae—a fungus with the ability to dissolve the soil P, resulting in slow release of P from MilorganiteTM treatment than from the other two treatments. Patterns of nutrient dynamics varied with rain and irrigation events under the effects of the soil water and time lapse of the amendment applications’ rates and types. All the macronutrients were present within the rootzone and leached below the rootzone, except Na. The dynamic of nutrients was element-specific and was influenced by the amendments’ type and application rate.

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Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island

Cited by 48 publications

References 25 publications

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

Impacts of Water Stress Severity and Duration on Potato Photosynthetic Activity and Yields

Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates

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