Tuber number is an essential factor determining yield and commodity in potato production. The initiation number has long been considered the sole determinant of the final total tuber number. In this study, we observed that tuber numbers at harvest were lower than at the tuber bulking stage; some formed tubers that were smaller than 3 cm degraded during development. Carbohydrate metabolism plays a crucial role in tuber degradation by coordinating the source–sink relationship. The contents of starch and sucrose, and the C:N ratio, are dramatically reduced in degradating tubers. Transcriptomic study showed that “carbohydrate metabolic processes” are Gene Ontology (GO) terms associated with tuber degradation. A polysaccharide degradation‐related gene, LOC102601831, and a sugar transport gene, LOC102587850 (SWEET6a), are dramatically up‐regulated in degradating tubers according to transcriptomic analysis, as validated by qRT‐PCT. The terms “peptidase inhibitor activity” and “hydrolase activity” refer to the changes in molecular functions that degradating tubers exhibit. Nitrogen supplementation during potato development alleviates tuber degradation to a certain degree. This study provides novel insight into potato tuber development and possible management strategies for improving potato cultivation.
Water deficiency is the main bottleneck in potato production in many regions worldwide. The generation of higher tuber yields per unit of water is a key goal for both agronomists and potato growers. In this study, we found that under moderate deficit irrigation (DI; 50% relative water content (RWC)) at the seedling stage of potato growth, the leaf area index (LAI) and dry matter accumulation were lower than control; however, they caught up with and surpassed the control at later developmental stages with a normal water supply, and a higher yield was ultimately achieved. The LAI and total dry weight under severe water stress (35% RWC) also surpassed the control at harvest; however, the final yield remained low, due to the low distribution of dry matter into the tubers. Abscisic acid (ABA) increased under DI conditions at the seedling stage, while gibberellin (GA1 and GA3) levels decreased. Moreover, endogenous ABA increased as plant development proceeded from seedling stage to tuber initiation stage, regardless of water stress. Exogenous ABA application promoted dry matter accumulation and distribution into the tubers. Therefore, it may be that ABA, as a major signaling molecule, mediates water stress to regulate tuber sink capacity at early development period. Through a feedback regulation stronger source capacity was stimulated by sink enhancement mediated by moderate water stress at the seedling stage, reached a higher tuber yield finally by reestablishment of sink-source relationship.
Proper water supply is crucial for high-yielding and high-quality potato tubers. Therefore, the accurate monitoring of potato water and precision irrigation based on water scarcity information has important practical significance for potato water-saving management. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, we measured the canopy spectral reflectance and plant water content under five irrigation treatments. The characteristic spectral parameters that responded to plant water status were selected, and a hyperspectral moisture diagnosis model of the potato leaf water content (LWC) and aboveground water content (AGWC) was established. We found that both the potato LWC and AGWC significantly decreased with increasing water stress. The potato canopy hyperspectral reflectance peak appeared in the red edge region, and this area’s reflectance varied significantly under different water treatments and decreased with decreased irrigation. Six potato moisture monitoring models with the sensitive band, first derivative, and water spectral index were established. The R2 values of the partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models between the LWC and hyperspectral data were 0.8418, 0.9020, and 0.8926, respectively. The R2 of the PLSR, SVM, and BP models between the AGWC and the hyperspectral data reached 0.8003, 0.8167, and 0.8671, respectively. All of the six models can realize the prediction of potato plant water content, but SVM was the best model for predicting the LWC of potato. These results will be highly significant in guiding the precision irrigation of different growth stages of potatoes.
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