Trends in the variability of potato tuber yield under selected land and soil characteristics

Abstract

The objective of this study was to evaluate variations in potato tuber yield with the selected land and soil characteristics including (i) topography (elevation and slope); (ii) geometry (horizontal coplanar geometry (HCP)and perpendicular coplanar geometry (PRP)); (iii) hydrology (volumetric moisture content; θv), and (iv) chemistry (phosphate, potash, organic carbon; organic carbon and pH). Analyses of the data collected across four potato fields, two each in Prince Edward Island and New Brunswick (NB), showed that the tuber yield negatively correlated with the field slope and positively correlated with most of the soil characteristics studied. Field elevation affected yield only under certain conditions such as higher range of elevation (≥ 7 m) (Field 2 in NB). Among soil characteristics, only HCP and PRP correlated with field elevation. The slope and elevation explained 22% to 36% variability of yield. Investigations of yield and topography by zonal analysis showed that yield was lower in zones of higher slope or elevation and lower θv, as the mean θv decreased in zones with a higher slope.

Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields

“…The values of SOM and pH reported in Tables 1, 3 and 4 of this study are in the neighborhood of the values reported by Farooque et al [31], measured in two potato fields of New Brunswick and two in PEI. Moreover, the values of P and K reported by Zare et al [56] that resulted in enhanced tuber yield in potato fields of PEI and New Brunswick are in the range of the values of P and K of excellent and medium zones, or high yield producing portions of Fields 1 and 2 of the current study. Stark et al [58] commended that potatoes have high nutrient demands, as they require approximately 247 kg of N, 33.5 kg of P, and 335 kg of K per hectare to produce approximately 56,491 kg/ha yield of Russet Burbank in southern Idaho, USA.…”

“…Soil organic matter and NDVI were the least significant in the field. Since higher values of HCP, θ, pH, K, P, Fe, and CEC determine good soil health for potato production [30,56] their presence in the excellent zone ensures higher tuber yield in excellent zones as compared to the poor and medium zones [29,31]. The tuber yield obtained from Fields 1 and 2 was higher than the country average vales of tuber yield of nations from Europe and America that adopt the best nutrient management practices, as tabulated by Koch et al [57].…”

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

“…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.…”

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

“…Maqsood et al [32] reported that climate extreme indices accounted for about 39% of the tuber yield change, while the rest of the variation in tuber yield was explained by the other factors, such as better management practices, better seed, fertilization, precision agriculture technology, field topography, soil properties physical, chemical and hydrologic properties, supplement irrigation, and others. Soil moisture explained 57-66% of variations in the potato tuber yield of experimental potato 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.…”

“…No major effects of different algorithms were apparent for PE-2018 dataset; however, the k-NN algorithm performed unexpectedly better in comparison with other datasets (Figure 4) as there were less correlated variables for this dataset (Figure 2). 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.…”