An open-source model is a model that makes it possible to modify the source code. This tool can be a great advantage for the user since it allows changing or modifying some of the background theory of the model. World Food Studies (WOFOST) and AquaCropOS open-source crop models were compared using field recorded data. Both models are free open-source tools that allow evaluating the impacts of climate and water on agriculture. The objective of this research was to assess the model’s efficiency in simulating the yield and above-ground biomass formation of a potato crop on the Cundiboyacense plateau. WOFOST simulates biomass accumulation in the crop organs using partitioning of assimilates to establish the biomass fraction that turns into yield. AquaCropOS simulates total above-ground biomass accumulation using crop water productivity (WP) and considers the Harvest Index (HI) to calculate yield formation. Crop modules for both models were built using information recorded in previous studies by other authors; those works performed a physiological and phenological characterization of some potato varieties. It was found that the WOFOST model simulates yield formation better than AquaCropOS; despite that, AquaCropOS simulates total above-ground biomass better than WOFOST. However, AquaCropOS was as efficient as WOFOST in simulating yield formation.

The response of the citrus crop to environmental supply largely determines the speed and intensity of the plant's ecophysiological processes, which affect the development and production of the crop. The main objective was to analyze the effects of climatic conditions on the productivity of the ‘Valencia’ orange agroecosystems (Citrus sinensis L. Osbeck) previously typified in the department of Meta, Colombia. The climatological variables precipitation (PPT), maximum and minimum temperatures (Tmax and Tmin), wind speed, relative humidity and solar brightness were analyzed in an observation window spanning the years 2013 to 2015. Using the FAO CropWat model, the crop reference evapotranspiration (ETo) was obtained to applied agroclimatic indices. Using the statistical software STATGRAPHICS Centurion XVI v. 16.2.04, an empirical model was proposed that relates productivity according to agroclimatic indices, for the vegetative and reproductive phenological phases. It was found that the proposed empirical model explains 49% (P=0.0233) of the oscillation of productivity in study area agroecosystems. The model, based on agroclimatic indices associated with PPT, ETo, Tmax and Tmin, found that the relationship between productivity and agroclimatic indices is non-linear. It was established that productivity variation is mainly influenced by PPT, the occurrence and magnitude of which determines the volume of production and quality of the fruit. On the other hand, whereas increases in air temperature and the occurrence of water deficits in the pre-flowering and flowering phases positively favor crop production, the same factors produce a negative effect in the setting phase.

Seasonal dynamics in edaphic humidity are influenced by different environmental factors, such as topography, physical and chemical soil conditions, type of vegetation cover and climatic classification. Data from 105 agrometeorological stations in the IDEAM network, distributed throughout Colombia, with records from January, 2001 to April, 2020, were studied. A non-parametric Spearman rank correlation test was used to evaluate the relationship between soil moisture and atmospheric variables. Simultaneously, the behaviors of seasonal dynamics were analyzed, along with their interaction with atmospheric, physical soil and vegetation cover variables. The results showed that soil moisture is more significantly influenced by frequency than by intensity of precipitation; this variable had a seasonal behavior, similar to that of precipitation. The physical variable texture was closely related to the behavior of the soil surface moisture (<10 cm deep). In addition, there was evidence of a surface moisture response to the physical conditions of the soil, topography and availability of plant cover. As the soil depth increased, the soil moisture had less variation because the influence of the atmospheric conditions was greater on the surface and persisted longer over time.

Methodological criteria for data quality control with geophysical range and spectrum consistency were evaluated, establishing flags and quality indicators for soil moisture data records, in a range of depths between 10, 30, and 50 cm, from automatic agro-meteorological stations located in the most important agricultural regions of Colombia. Data for analysis were collected from 105 stations of the IDEAM network, in an observation window from 2001-2020. The results showed that 40.3% of the soil moisture data were of good quality, 12.9% were questionable due to spectrum flags, 14.3% were questionable due to geophysical range and 32% were erroneous because the values were not possible and/or missing. The depth closest to the surface had the highest number of quality flags, suggesting that the soil layer has the highest error detection rate associated with soil moisture condition recording; the most common quality flag was C02: “Soil moisture >60% & ≤100%”, detected in 93% of the sensors, and the second most frequent flag was C01: “Soil moisture ≥0% & <3%”. It was concluded that the proposed methodology provides highly satisfactory results in the detection of anomalous soil moisture records, in order to make adjustments to the environmental conditions of Colombia.