Exploring the optimization of water and fertilizer management practices for potato production in the sandy loam soils of Northwest China based on PCA

Wang, Xiukang; Guo, Tao; Yi, Wang; Xing, Yingying; Wang, Yanfeng; He, Xiaolong

doi:10.1016/j.agwat.2020.106180

Cited by 57 publications

(23 citation statements)

References 83 publications

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“…Sustainability 2020, 12, x FOR PEER REVIEW 3 of 15 2055 (-2 to -6%) but will have a larger impact by 2085 (−2 to −26%) depending upon the RCP as reported by Raymundo et al [28]. Tuber yield depends on many factors like soil and water management practices, chemical and bio-fertilization, seed quality, soil moisture contents, slope, elevation, and supplement irrigation [37][38][39][40][41][42][43]. These parameters are individually responsible for most of the variation (22-66) % in the tuber yield.…”

Section: Study Areamentioning

confidence: 99%

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Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island

et al. 2020

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Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen’s slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of −1.01 °C, −3.75 days, −5.67 days, −11.40 nights, and −2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months.

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Section: Study Areamentioning

confidence: 99%

“…Tuber yield depends on many factors like soil and water management practices, chemical and bio-fertilization, seed quality, soil moisture contents, slope, elevation, and supplement irrigation [37][38][39][40][41][42][43]. These parameters are individually responsible for most of the variation (22-66) % in the tuber yield.…”

Section: Introductionmentioning

confidence: 99%

Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island

et al. 2020

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“…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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“…Potato is also a short-duration crop that produces large and sensitive foliage in response to soil moisture stress [10,11]. Several studies have confirmed that small errors in irrigation water management in potato production and exposure of potato plants to deficit irrigation, particularly during the sensitive growth stages, causes a tremendous reduction in the quantity and quality of tuber yield and the net economic return of the production system [12][13][14][15][16][17][18]. Consequently, correct irrigation water management is required to simultaneously maximize the yield and irrigation water use efficiency of potato under deficit irrigation conditions [18].…”

Section: Introductionmentioning

confidence: 99%

Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

et al. 2021

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Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

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Exploring the optimization of water and fertilizer management practices for potato production in the sandy loam soils of Northwest China based on PCA

Cited by 57 publications

References 83 publications

Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island

Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

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