High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato.

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.

Potential Short-Term Memory Induction as a Promising Method for Increasing Drought Tolerance in Sweetpotato Crop Wild Relatives [Ipomoea series Batatas (Choisy) D. F. Austin].

Crop wild relatives of sweetpotato [Ipomoea series Batatas (Choisy) D. F. Austin] are a group of species with potential for use in crop improvement programs seeking to breed for drought tolerance. Stress memory in this group could enhance these species' physiological response to drought, though no studies have yet been conducted in this area. In this pot experiment, drought tolerance, determined using secondary traits, was tested in 59 sweetpotato crop wild relative accessions using potential short-term memory induction. For this purpose, accessions were subjected to two treatments, i) non-priming: full irrigation (up to field capacity, 0.32 w/w) from transplanting to harvest and ii) priming: full irrigation from transplanting to flowering onset (FO) followed by a priming process from FO to harvest. The priming process consisted of three water restriction periods of increasing length (8, 11, and 14 days) followed each by a recovery period of 14 days with full irrigation. Potential stress memory induction was calculated for each accession based on ecophysiological indicators such as senescence, foliar area, leaf-minus-air temperature, and leaf 13C discrimination. Based on total biomass production, resilience and production capacity were calculated per accession to evaluate drought tolerance. Increase in foliar area, efficient leaf thermoregulation, improvement of leaf photosynthetic performance, and delayed senescence were identified in 23.7, 28.8, 50.8, and 81.4% of the total number of accessions, respectively. It was observed that under a severe drought scenario, a resilient response included more long-lived green leaf area while a productive response was related to optimized leaf thermoregulation and gas exchange. Our preliminary results suggest that I. triloba and I. trifida have the potential to improve sweetpotato resilience in dry environments and should be included in introgression breeding programs of this crop. Furthermore, I. splendor-sylvae, I. ramosissima, I. tiliacea, and wild I. batatas were the most productive species studied but given the genetic barriers to interspecific hybridization between these species and sweetpotato, we suggest that further genetic and metabolic studies be conducted on them. Finally, this study proposes a promising method for improving drought tolerance based on potential stress-memory induction, which is applicable both for wild species and crops.

Radiation Interception, Conversion and Partitioning Efficiency in Potato Landraces: How Far Are We from the Optimum?

Crop efficiencies associated with intercepted radiation, conversion into biomass and allocation to edible organs are essential for yield improvement strategies that would enhance genetic properties to maximize carbon gain without increasing crop inputs. The production of 20 potato landraces-never studied before-was analyzed for radiation interception ( ε i ), conversion ( ε c ) and partitioning ( ε p ) efficiencies. Additionally, other physiological traits related to senescence delay (normalized difference vegetation index (NDVI) s l p ), tuberization precocity ( t u ), photosynthetic performance and dry tuber yield per plant (TY) were also assessed. Vegetation reflectance was remotely acquired and the efficiencies estimated through a process-based model parameterized by a time-series of airborne imageries. The combination of ε i and ε c , closely associated with an early tuber maturity and a NDVI s l p explained 39% of the variability grouping the most productive genotypes. TY was closely correlated to senescence delay (r P e a r s o n = 0.74), indicating the usefulness of remote sensing methods for potato yield diversity characterization. About 89% of TY was explained by the first three principal components, associated mainly to t u , ε c and ε i , respectively. When comparing potato with other major crops, its ε p is very close to the theoretical maximum. These findings suggest that there is room for improving ε i and ε c to enhance potato production.

Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.).

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( r P e a r s o n = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha - 1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m 3 ha - 1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.