Yield and quality of three potato cultivars under series of nitrogen rates

Abstract

Undesirable growth of potato (Solanum tuberosum L.) crop under an excessive N fertilizer application is the main obstacle presently. This research was conducted to investigate the response of different potato cultivars; Russet Burbank, Shepody, and Superior, and its qualitative characteristics under a series of N rates. Six rates of N fertilization (0–280 kg ha−1) were applied on 11 sites in a randomized complete block design, with four replications. Sites with ≥30 g kg−1 of soil organic matter (OM) produced total tuber yield, marketable yield, and tuber weight per plant 39.5, 45.2, and 54.9%, respectively, higher than sites with ≤30 g kg−1 of OM. Tubers specific gravity increased by 0.18% in the sites with ≥30 g kg−1 of OM. The total tuber yield for the three cultivars was maximized at 168 kg N ha−1. Marketable specific gravity, starch, and dry matter content were achieved by applying 168 and 112 kg N ha−1 at ≤30 and ≥30 g kg−1 of OM sites, respectively. Russet Burbank produced a higher yield than Shepody and Superior cultivars significantly, but there was no significant difference among them regarding specific gravity. Excessive N application (>168 kg ha−1) decreased potato tuber production and quality.

Potato Growth and Yield Characteristics under Different Cropping System Management Strategies in Northeastern U.S.

“…Although only one potato variety, Russet Burbank, which is the predominant processing variety grown commercially in the northeast, was used in this study, observed results should be generally applicable to other potato varieties as well. Previous studies in this region have indicated similar responses to rotations, amendments, and fertilization in multiple different potato varieties [29][30][31][32]36], and improvements in soil health have been associated with increases in yield across not only different potato varieties but many different crops as well [4,6,[12][13][14][15]44].…”

“…However, based on the results observed and the characteristics of these systems, it is apparent that improvements in properties associated with the ability to store and hold soil water were at least partially, if not primarily, responsible for the yield increases observed in the SI system, rather than such aspects as nutritional improvements. First, all systems were supplied with adequate (above optimal NPK) fertilization so as not to limit productivity based on numerous studies in this area [29][30][31][32][33]. Additional NPK fertility above these levels provided by the compost amendments would not be expected to increase yield further, as studies have indicated depressed yields, not increased yields, with above optimal fertilizer additions [29][30][31][32][33].…”

“…First, all systems were supplied with adequate (above optimal NPK) fertilization so as not to limit productivity based on numerous studies in this area [29][30][31][32][33]. Additional NPK fertility above these levels provided by the compost amendments would not be expected to increase yield further, as studies have indicated depressed yields, not increased yields, with above optimal fertilizer additions [29][30][31][32][33]. Most importantly, SI had higher yields than other systems when not irrigated, but no improvement with irrigation, even though all other systems showed increased yields when irrigated.…”

“…Potato plots were fertilized with the equivalent of 224 kg ha −1 N and 249 kg ha −1 P 2 O 5 and K 2 O. Fertilizer rate was based on years of previous research for this region in similar soils establishing 150-200 kg N ha −1 as optimal for potato production [29][30][31][32][33]. Fertilizer applications were purposely applied to be equal across all systems and above optimal rates for crop nutritional needs, so that nutrition would not be limiting in any cropping system and observed system effects would likely be related to factors other than fertility.…”

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

“…Selected VIs were calculated based on the multispectral UAV images (Table 2). These VIs were normalized using GDD (N VI ), which is particularly useful when combining data from different site-years and growth stages [10,34]. The N VI was computed by dividing the VI data by the accumulated GDD from planting to sensing, as shown in Equation (3).…”