Building capacity for assessing spatial-based sustainability metrics in agriculture

Crop yield is influenced over time and space, namely, by a wide range of variables linked with crop genetics, agronomic management practices and the environment under which the crop dynamically responds to maximize growth potential and survival. Such variability can pose substantial uncertainty and risks in the use of agricultural sustainability decision-making frameworks that include crop yield as a leading metric. Here, decision analytics can play a vital role by guiding the use of statistical-based analytics to build in a higher degree of intelligence to enable better predictive (i.e., crop yield forecasting both over the growing season and inter-annually) and prescriptive (optimization across crop areas and subdivisions) approaches. While inter-annual variability in yield can be modelled based on a deterministic trend with stochastic variation, quantifying the variability of yield and how it changes across different spatial resolutions remains a major knowledge gap. To better understand how yield scales spatially, we integrate in this study, for the first time, multi-scale crop yield of spring wheat and its variance (i.e., field to district to region) obtained within the major wheat growing region of the Canadian Prairies (Western Canada). We found large differences between the mean and variance from field to district to regional scales, from which we determined spatially-dependent (i.e., site specific) scaling factors for the mean and variance of crop yield. From our analysis, we provide several key recommendations for building capacity in assessing agricultural sustainability using spatial-based metrics. In the future, the use of such metrics may broaden the adoption and consistent implementation of new sustainable management protocols and practices under a precautionary, adaptive management approach

SCALE-INVARIANCE OF NORMALIZED YEARLY MEAN GRAIN YIELD ANOMALY SERIES

In crop science, tools of non-linear dynamics, fractals, chaos, intermittency and self-organized criticality may be employed and applied to the analysis of spatial variability and temporal behavior of agro-meteorological variables, soil properties, plant attributes, commercial yields, and prices of the agricultural products in order to gain knowledge about underlying complex processes. A search on the occurrence of particular scaling laws in Mexico's normalized yearly mean grain yield anomaly series of maize (Zea mays L.), beans (Phaseolus vulgaris L.), wheat (Triticum aestivum L.) and rice (Oriza sativa L.), using a variography approach is reported in this work. Additionally, power spectrum determination, time-frequency analysis, and estimation of Lyapunov exponent were performed for each profile in order to obtain useful information on the frequency contents and signs at which important frequencies occur as well as to determine their sensitivity to initial conditions. Fractal analysis gives us the order maizeCrop science, crop yield series, ENSO, QBO, Lyapunov exponent

Investigación en matemáticas, economía y ciencias sociales

El resultado de este libro que reune inquietudes académicas en torno a temas tan estudiados como los que están alrededor del maíz, del frijol o del café; y tan contemporáneos como las aplicaciones concretas de las ciencias ya citadas, al estudio de la adopción del comercio electrónico en empresas del sector agroindustrial o, el caso de la generación de biogas o energía eléctrica por medio de biodigestores. Al editar este texto e incorporarlo a la bibliografía de los temas de referencia, se enriquecen opciones de consulta para los estudiosos de esos temas en general; pero también para interesados en aspectos tan específicos como la cadena de suministro del mercado hortofrutícola en Texcoco