Urban agriculture refers to any type of activity located within or around a city designed to provide ecosystem services. Given the rapid population growth and urbanization, urban agriculture is seen as a potential alternative route to a more sustainable urban food system. This review answers the main question: What are the benefits of non-commercial of Urban Agriculture (NCUA) forms and its contribution towards food production? using a systematic literature review approach. The methodology involved capturing 1355 recent articles from qualified search engines, using key terms according to the defined question, then screened for relevance and the defined scope of this review, resulting in a final selection of 40 articles for analysis. The results show that implementing NCUA practices has multifaced social, economic, and environmental benefits, such as improving people’s health, reducing expenditure on food and creating sustainable cities, highlighting the need to recognize the multifaceted role of NCUA in promoting a more sustainable lifestyle and strengthening local communities and engagement. Moreover, awareness of urban agriculture differs between developed and developing countries, as does the recognition and valorization of its benefits. Further research is needed to examine the enabling factors and barriers to NCUA adoption in different urban context, the resource implications, and the long-term sustainability of these practices.

Ecuador is one of the largest banana exporters in the world. This sector generates wealth and employment in the country. Life cycle method tools support finding critical points and improvement measures in systems. In this study, the Ecuadorian banana is evaluated through life cycle assessment (LCA), including agriculture, packaging, transfer to the Port of Guayaquil, and transport to a foreign port. OpenLCA software was used, applying the Recipe Midpoint (H) V1.13 impact evaluation method and using primary data collected from a local producer and secondary data from Ecoinvent 3.6 databases, Agribalyse 3.0.1, and the literature. Functional units were established at three levels: “1 ton of Banana at-the-farm-gate”; “1 ton of Banana at-the-packaging-stage-gate”; and “1 ton of Banana at-the-port-of-destination”. The impact categories evaluated are climate change (GWP100), fossil depletion (FDP), freshwater eutrophication (FEP), marine eutrophication (MEP), ozone layer depletion (ODPinf), particulate matter formation (PMFP), formation of photochemical oxidants (POFP), and terrestrial acidification (TAP100). The carbon footprint (GWP100) of “Banana at-the-farm-gate”, “Banana at-the-packaging-stage-gate”, and “Banana at-the-foreign-port” ranged from 194 to 220, 342 to 352, and 615.41 to 625.44 kg CO₂-Eq/Ton banana, respectively. Hotspots of the system are the fertilizer field emissions, cardboard packaging, rachis disposal, and maritime transport. Improvement measures should focus on reducing the amount of fertilizers and developing circular alternatives for residual biomass valorization.

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m²·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m²·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

Agrometeorological stations have horizontal solar irradiation data available, but the design and simulation of photovoltaic (PV) systems require data about the solar panel (inclined and/or oriented). Greenhouses for agricultural production, outside the large protected production areas, are usually off-grid; thus, the solar irradiation variable on the panel plane is critical for an optimal PV design. Modeling of solar radiation components (beam, diffuse, and ground-reflected) is carried out by calculating the extraterrestrial solar radiation, solar height, angle of incidence, and diffuse solar radiation. In this study, the modeling was done using Simulink-MATLAB blocks to facilitate its application, using the day of the year, the time of day, and the hourly horizontal global solar irradiation as input variables. The rest of the parameters (i.e., inclination, orientation, solar constant, albedo, latitude, and longitude) were fixed in each block. The results obtained using anisotropic models of diffuse solar irradiation of the sky in the region of Castile and León (Spain) showed improvements over the results obtained with isotropic models. This work enables the precise estimation of solar irradiation on a solar panel flexibly, for particular places, and with the best models for each of the components of solar radiation.

Organic solid wastes are rarely considered when planning for rural production in Argentina. Onion production in the low valley of Río Colorado (Buenos Aires) generates between 12,000 and 20,000 Mg year⁻¹ of vegetal wastes (i.e., leaves, stems, skins, roots) from harvesting, cleaning and classification of bulbs, causing many problems with their management. The aim of this work is to study the effect of different doses of onion residue-bovine manure compost and onion residue-bovine manure compost tea on the soil physicochemical properties, microbial activity and agroecological onion production in sandy soil. Results showed that the highest dose of compost caused the highest effects on soil pH, electrical conductivity and nutrient content. Soil enzymatic activities were already high in the soil before the compost was applied, which may have contributed to the small effect caused by any dose on soil activity. A significant positive effect on bulb weight and organic onion yield were found as a result of the amendment and growing season. In conclusion, agroecological production of onion with the addition of a 300 kg N ha⁻¹ compost and compost tea guarantee yields comparable to those of conventional fertilization, as occurred during the two growing seasons of this study.

This study evaluates the predictive modeling of the daily ambient temperature (maximum, T_max; average, T_ave; and minimum, T_min) and its hourly estimation (T_0h, …, T_23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (T_max(t + 1), T_ave(t + 1), T_min(t + 1)). Two models were evaluated: (1) with three entries (T_max(t), T_ave(t), T_min(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for T_max = 2.56, T_ave = 1.65 and T_min = 2.09 (°C), achieving better results than the classical statistical methods (typical year T_ave = 3.64 °C; weighted moving mean T_max = 2.76, T_ave = 1.81 and T_min = 2.52 (°C); linear regression T_ave = 1.85 °C; and Fourier T_max = 3.75, T_ave = 2.67 and T_min = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T_0h(t), …, T_23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.