Seed uniformity and stability testing, and multiplication, are key steps in the seed supply chain of the common bean (Phaseolus vulgaris L.) and other crops. Optimizing agronomical practices in these phases can ultimately ensure seed quality and availability, and germplasm prospective utilization. However, farmers have rarely standardized seed testing and propagation protocols in local common bean landraces conserved in situ. An example of this is the Liborino variety (var.), a promising yellow Andean common bean known for its presumably high digestibility and adaptation to the local conditions of the Cauca river canyon (northwest Andes of Colombia), but likely experiencing genetic erosion after decades of suboptimal propagation. Therefore, this work intended to evaluate and select locally adapted genotypes of common bean var. Liborino for commercial use, to be later multiplied, evaluated by participatory breeding, and eventually shared with farmers. Specifically, we evaluated 44 accessions of var. Liborino common bean in six adaption and yield field trials in the Cauca river canyon at 1100 and 1400 m a.s.l, and in AGROSAVIA’s “La Selva” research station at 2100 m a.s.l. In parallel, we carried out standardized seed multiplication of a Liborino genotype using best practices to guarantee uniformity and stability. From the 44 accessions, nine were well adapted to the tested local conditions. Four of these accessions exhibited a bush type growth habit, while the remaining five were climbers. The trials revealed maximum average extrapolated yields of up to 1169.4 ± 228.4 kg ha⁻¹ for the bush types (G8152) and up to 1720.0 ± 588.4 kg ha⁻¹ for the climbers (G51018), both at 2100 m a.s.l. Three climbing accessions matched farmers’ expectations for seed coat color and shape, according to a participatory selection exercise. Uniform and stable seed of the selected genotype was delivered in 2022 to 39 farmers, ~6.5 kg of seeds per farmer. Our results will allow implementing bean genetic improvement pipelines, promoting var. Liborino commercialization, and boosting the economic and sustainable development of the rural communities in the Cauca river canyon. Seed uniformity testing and multiplication pipelines must be extended to other bean landraces conserved in situ.

Evaluation of sowing density is an important factor for achieving maximum yields without affecting other agronomic traits. Field experiments were conducted during three consecutive years (2008, 2009 and 2010) to determinate the effect of four sowing density (62,500; 83,333; 100,000 and 142,857 pl ha⁻¹) on yields and its components of two cotton varieties, ‘Delta Pine 16′ and ‘SN-290′ in Venezuela. The traits evaluated were lint yield, boll weight, number of seeds per boll, 100-seed weight, and fiber content. Highly significant differences (p ≤ 0.01) were observed among genotypes, sowing density and their interactions for all traits. Sowing density was not affected by year factor. High lint yield was found in ‘SN-290′ (4216.2 kg ha⁻¹) at 100,000 pl ha⁻¹; and in ‘Delta Pine 16′ (3917.3 kg ha⁻¹) at 83,333 pl ha⁻¹. The highest sowing density (142,857 pl ha⁻¹), decrease lint yield and yield components in the genotypes. The highest boll weight was obtained by ‘SN-290′ with 6.4 g in average. All sowing densities evaluated resulted in lint percentages above 40%. Cotton lint yield was positively correlated with all yield components. Our results indicate that highest lint yields could be obtained with sowing densities between 83,333 and 100,000 pl ha⁻¹ depending upon varieties used across savannahs of Venezuela.

The avocado cv. Hass requires a suitable rootstock for optimal development under water stress. This study evaluated the performance of two avocado rootstocks (ANRR88 and ANGI52) grafted onto cv. Hass under four water stress conditions, 50% and 25% deficit, and 50% and 25% excess during the nursery stage. Plant height, leaf area (LA), dry matter (DM), and Carbon (OC) content in the roots, stems, and leaves were measured. Root traits were evaluated using digital imaging, and three vegetation indices (NDVI, CI_RE, and MTCI) were used to quantify stress. The results showed that genotype significantly influenced the response to water stress. ANRR88 exhibited adaptation to moderate to high water deficits. ANGI52 adapted better to both water deficit and excess, and showed greater root exploration. LA and DM reductions of up to 60% were observed in ANRR88, suggesting a higher sensitivity to extreme changes in water availability. More than 90% of the total OC accumulation was observed in the stem and roots. The NDVI and the MTCI quantified the presence and levels of stress applied, and the 720 nm band provided high precision and speed for detecting stress. These insights are crucial for selecting rootstocks that ensure optimal performance under varying water availability, enhancing productivity and sustainability.

Maize crops occupy an important place in world food security. However, different conditions, such as abiotic stress factors, can affect the productivity of these crops, requiring technologies that facilitate their monitoring. One such technology is spectroscopy, which measures the energy reflected and emitted by a surface along the electromagnetic spectrum. Spectral data can help to identify abiotic factors in plants, since the spectral signature of vegetation has discriminating features associated with the plant’s health condition. This paper introduces a spectral library captured on maize crops under different nitrogen-deficiency stress levels. The datasets will be of potential interest to researchers, ecologists, and agronomists seeking to understand the spectral features of maize under nitrogen-deficiency stress. The library includes three datasets captured at different growth stages of 10 tropical maize genotypes. The spectral signatures collected were in the visible to near-infrared range (450–950 nm). The data were pre-processed to reduce noise and anomalous signatures. This study presents a spectral library of the effects of nitrogen deficiency on ten maize genotypes, highlighting that some genotypes show tolerance to this type of stress at different phenological stages. Most of the evaluated genotypes showed discriminate spectral features 4–6 weeks after sowing. Higher reflectance was obtained at approximately 550 nm for the lowest nitrogen fertilization treatments. Finally, we describe some potential applications of the spectral library of maize leaves under nitrogen-deficiency stress.