This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The objective of the work was to evaluate and compare the physicochemical characteristics of an experimental cabotiá hybrid with the commercial hybrid Tetsukabuto. The genotypes were divided according to mass (kg), and were evaluated for quality. The color parameters evaluated showed no significant difference, although visually the hybrid was different from the commercial variety. It was possible to conclude that the size of the fruits does not influence the concentration of the compounds, and also, an inferiority of HC05 was observed with respect to the relevant quality characteristics in pumpkins, such as soluble solids content, carotenoids and vitamin C.

Forests have ecological functions in water conservation, climate regulation, environmental purification, soil and water conservation, biodiversity protection and so on. Carrying out forest ecological quality assessment is of great significance to understand the global carbon cycle, energy cycle and climate change. Based on the introduction of the concept and research methods of forest ecological quality, this paper analyzes and summarizes the evaluation of forest ecological quality from three comprehensive indicators: forest biomass, forest productivity and forest structure. This paper focuses on the construction of evaluation index system, the acquisition of evaluation data and the estimation of key ecological parameters, discusses the main problems existing in the current forest ecological quality evaluation, and looks forward to its development prospects, including the unified standardization of evaluation indexes, high-quality data, the impact of forest living environment, the acquisition of forest level from multi-source remote sensing data, the application of vertical structural parameters and the interaction between forest ecological quality and ecological function.

The propagation of plant material in the arracacha crop is commonly done vegetatively through asexual seed, this activity has allowed its multiplication and conservation over time. The plant material available is of low quality, affecting the development and potential yield of the crop and therefore the producer’s income. The objective of the research was to comparatively analyze two technologies for the production of arracacha seed: local technology and Agrosavia technology. The information for the local technology was obtained from surveys applied to farmers and the selection was made using the deterministic sampling technique, and for the Agrosavia technology through the recording of data and production costs in research lots at commercial scale. Descriptive statistics and calculation of economic return indicators were applied for the two situations. The results show that the use of quality seed allows obtaining higher seed production (251,559 unit ha^-1) and tuberous roots (25,875 kg ha^-1), being superior to local technology by 14% and 28% respectively; thus, the arracacha producer acquires greater economic efficiency by obtaining lower unit cost per kilo produced and better net income with a marginal rate of return of 316.45. The results achieved are useful for farmers, companies and entities that wish to produce quality seed and support the arracacha production system in Colombia.

The use of saline water in agriculture is a viable alternative, considering the increased demand for fresh water. The objective of this study was to evaluate the growth and phytomass production of sugar beet under irrigation with water of different saline concentrations in a field experiment on the campus of the Federal University of Alagoas in Arapiraca. The treatments were five levels of electrical conductivity (1.0, 2.0, 3.0, 4.0 and 5.0 dS m^-1). The design was in randomized blocks, with four repetitions. The maximum yield of sugar beet at 27 days after the application of saline treatments was obtained with a salinity of 3.0 dS m^-1, for the variables plant height (PA), stem diameter (CD), root length (RC), aboveground dry phytomass (FSPA) and total dry phytomass (FST). At 42 days after the application of saline treatments, the variables aboveground fresh phytomass (FFPA), root fresh phytomass (FFR), total fresh phytomass (FFT), aboveground dry phytomass (FSPA) and total dry phytomass (FST) increased with increasing water salinity. Rain may have influenced the results obtained for the evaluations, performed at 42 days after the application of the saline treatments.