Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change.

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917-1946 and 1991-2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m-1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (∼17 mm m-1) and decrease in September (∼14 mm m-1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

Preliminary Studies to Characterize the Temporal Variation of Micronutrient Composition of the Above Ground Organs of Maize and Correlated Uptake Rates.

The improvement of agronomic practices and the use of high technology in field crops contributes for significant increases in maize productivity, and may have altered the dynamics of nutrient uptake and partition by the plant. Official recommendations for fertilizer applications to the maize crop in Brazil and in many countries are based on critical soil nutrient contents and are relatively outdated. Since the factors that interact in an agricultural production system are dynamic, mathematical modeling of the growth process turns out to be an appropriate tool for these studies. Agricultural modeling can expand our knowledge about the interactions prevailing in the soil-plant-atmosphere system. The objective of this study is to propose a methodology for characterizing the micronutrient composition of different organs and their extraction, and export during maize crop development, based on modeling nutrient uptake, crop potential evapotranspiration and micronutrient partitioning in the plant, considering the production environment. This preliminary characterization study (experimental growth analysis) considers the temporal variation of the micronutrient uptake rate in the aboveground organs, which defines crop needs and the critical nutrient content of the soil solution. The methodology allowed verifying that, initially, the highest fraction of dry matter, among aboveground organs, was assigned to the leaves. After the R1 growth stage, the largest part of dry matter was partitioned to the stalk, which in this growth stage is the main storage organ of the maize plant. During the reproductive phase, the highest fraction of dry matter was conferred to the reproductive organs, due to the high demand for carbohydrates for grain filling. The micronutrient (B, Cu, Fe, Mn, and Zn) content follows a power model, with higher values for the initial growth stages of development and leveling off to minimum values at the R6 growth stage. The proposed model allows to verify that fertilizer recommendations should be related to the temporal variability of micronutrient absorption rates, in contrast to the classic recommendation based on the critical soil micronutrient content. The maximum micronutrient absorption rates occur between the reproductive R4 and R5 growth stages. These evaluations allowed to predict the maximum micronutrient requirements, considered equal to respective stalk sap concentrations.