Dissection of the genetic architecture of adaptation and abiotic stress-related traits is highly desirable for developing drought-tolerant potatoes and enhancing the resilience of existing cultivars, particularly as agricultural production in rain-fed areas may be reduced by up to 50 % by 2020. The “DMDD” potato progeny was developed at International Potato Center (CIP) by crossing the sequenced double monoploid line DM and a diploid cultivar of the Solanum tuberosum diploid Andigenum Goniocalyx group. Recently, a high-density integrated genetic map based on single nucleotide polymorphism (SNP), diversity array technology (DArT), simple sequence repeats (SSRs), and amplified fragment length polymorphism (AFLP) markers was also made available for this population. Two trials were conducted, in greenhouse and field, for drought tolerance with two treatments each, well-watered and terminal drought, in which watering was suspended 60 days after planting. The DMDD population was evaluated for agro-morphological and physiological traits before and after initiation of stress, at multiple time points. Two dense parental genetic maps were constructed using published genotypic data, and quantitative trait locus (QTL) analysis identified 45 genomic regions associated with nine traits in well-watered and terminal drought treatments and 26 potentially associated with drought stress. In this study, the strong influence of environmental factors besides water shortage on the expression of traits and QTLs reflects the multigenic control of traits related to drought tolerance. This is the first study to our knowledge in potato identifying QTLs for drought-related traits in field and greenhouse trials, giving new insights into genetic architecture of drought-related traits. Many of the QTLs identified have the potential to be used in potato breeding programs for enhanced drought tolerance.

Access to Water and Nitrogen (N) are key challenges in potato crop management that could be optimized through understanding related agronomic and physiological traits. The responses to water deficit and two levels of nitrogen (0 and 200 kg N.ha-1) were assessed here for agronomic and physiological traits for potato genotypes UNICA, Achirana-INTA, and CIP397077.16, chosen for their short vegetative period (90--110 days) and tolerance to water deficit. Nitrogen Use Efficiency (NUE) was greatly reduced in all genotypes under drought, even dropping to nearly zero. Despite this, the tuber yield and total biomass reduction of around 50 % for all genotypes could be alleviated with N application. Normalized Difference Vegetation Index (NDVI), chlorophyll content (SPAD), and Canopy cover (CC) were found to be good indicators for predicting nitrogen deficits and detecting early stress by drought. Overall, each genotype had different strategies to manage N and cope with drought stress. UNICA was the most efficient genotype at using the N present in the soil and tolerating drought stress, while Achirana-INTA was most affected by drought in terms of NUE. Based on the results, it is suggested that maximum fertilizer use efficiency can be obtained with low N rates applied under well-watered conditions. Improved NUE and a greater understanding of the physiological response of crops to drought stress and N fertilization will help to reduce fertilizer use, lessening the effects of water contamination and improving input efficiency.Resumen El acceso al agua y al nitrógeno (N) son retos clave en el manejo del cultivo de la papa, que pudieran optimizarse a través del entendimiento de aspectos agronómicos y fisiológicos relacionados. Aquí se analizaron las respuestas al déficit de agua y a dos niveles de nitrógeno (0 y 200 kg N ha −1 ) para rasgos agronómicos y fisiológicos para los genotipos de papa UNICA, Achirana-INTA y CIP397077.16, seleccionados por su período vegetativo corto (90-110 días) y su tolerancia al déficit de agua. La eficiencia en el uso del nitrógeno (NUE) se redujo grandemente en todos los genotipos bajo sequía, aun bajando a cerca de cero. A pesar de esto, el rendimiento de tubérculo y la reducción total de biomasa de cerca del 50 % para todos los genotipos pudo superarse con la aplicación de N. Se encontró que el Índice de la Diferencia Normalizada de Vegetación (NDVI), el contenido de clorofila (SPAD) y la cubierta del follaje (CC), fueron buenos indicadores para la predicción de los déficits de nitrógeno y para detectar agobio temprano por sequía. Sobre todo, cada genotipo tuvo diferentes estrategias para manejar el N y lidiar con el agobio hídrico. UNICA fue el genotipo más eficiente en el uso del N presente en el suelo y en la tolerancia al estrés por sequía, mientras que Achirana-INTA fue el más afectado por sequía en términos de NUE. Con base en los resultados, se sugiere que la máxima eficiencia en el uso de fertilizante se puede obtener con niveles bajos de N aplicados en condiciones de buena hi...

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

The effects of gamma radiation on the germination, survival, and morphological damage in characteristics of the M1 generation of coffee (Coffea arabica L. var. typica) plants was evaluated using seeds collected from Santa Teresa and Chaupimayo, Peru. Under net house, the percentage of germination was 68%, 35%, 10%, and 0% for the Santa Teresa seeds and 75%, 49%, 17% and 0% for the Chaupimayo seeds with irradiation treatments of 0, 50, 100 and 150 Gy, respectively, whereas under laboratory conditions, germination levels were between 70% and 94% across all treatments. The survival rate also decreased with increasing radiation levels, with values of 45%, 32%, 28%, and 10% in the laboratory and 29%, 9%, 6%, and 0% in the net house for the Santa Teresa seeds and 58%, 45%, 38%, and 8% in the laboratory and 42%, 15%, 7%, and 0% in the net house for the Chaupimayo seeds with irradiation treatments of 0, 50, 100, and 150 Gy, respectively. Morphological changes were observed in the plants that survived irradiation at a dose of 100 Gy in terms of the leaf shape, leaf apex shape, young leaf color, plant height, stem diameter, number of leaves per plant, leaf length, leaf width, and distance from the cotyledon to the first node. ResumenSe evaluó los efectos de la radiación gamma sobre la germinación, la supervivencia y características morfológico de plantas de café de generación M1 (Coffea arabica L. var. typica), utilizando semillas colectadas de Santa Teresa y Chaupimayo, Perú. En casa malla, el porcentaje de germinación fue del 68%, 35%, 10% y 0% para las semillas de Santa Teresa y 75%, 49%, 17% y 0% para las semillas de Chaupimayo con tratamientos de irradiación de 0, 50, 100 y 150 Gy, respectivamente, mientras que en condiciones de laboratorio, los niveles de germinación fueron entre 70% y 94% en todos los tratamientos. El porcentaje de supervivencia también disminuyó con el aumento de los niveles de radiación, con valores de 45%, 32%, 28% y 10% en el laboratorio y 29%, 9%, 6% y 0% en la casa malla para las semillas de Santa Teresa y 58 %, 45%, 38% y 8% en el laboratorio y 42%, 15%, 7% y 0% en la casa malla para las semillas de Chaupimayo con tratamientos de irradiación de 0, 50, 100 y 150 Gy, respectivamente. Se observaron cambios morfológicos en las plantas que sobrevivieron a la irradiación a una dosis de 100 Gy en características como forma y ápice de la hoja, color de la hoja joven, altura de la planta, diámetro del tallo, número de hojas por planta, longitud de la hoja, ancho de la hoja y distancia del cotiledón al primer nudo.

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Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.

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Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2 ) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.