Predictions and Estimations in Agricultural Production under a Changing Climate—Volume II

Dear Colleagues,

Prediction is the rational and scientific anticipation of future events in order to reduce risk in the decision-making process. Prediction in today’s agriculture is a very important aspect of improving and refining the management of any agricultural activity. Predictive analytics is increasingly being used in agriculture not only to describe large-scale processes but also at the scale of individual crop fields. The results of such forecasts can help to decide on many current activities during the growing season, including the date of harvesting or plant protection treatments. Up-to-date forecasts make it possible to monitor the prepared storage area and estimate the necessary inputs. Forecasting is becoming increasingly important under climate change. These inevitable changes have a huge impact on the transformation of ecosystems, both natural and under strict human control. Taking into account the above arguments, it is worth developing systems for reliable monitoring and prediction of multistage agricultural production, which will allow, among other things, for estimating in advance the possible production effects to be achieved in both atypical years and standard conditions. Simulations of processes occurring in food production help us to understand the combined effects of water and nutrient deficiencies, pests, diseases, the impact of yield variability, and other field conditions during the growing season. In other words, they integrate multiple factors affecting the final production outcome with relatively low prediction error. Currently, tools supporting prediction in agriculture include classical statistical models, machine learning, GIS tools, satellite and aerial remote sensing, the Internet of Things, and big data. The abovementioned techniques have become allies of decision makers in key decision-making processes, supporting industry databases with relevant information necessary in the process of managing and monitoring agricultural production.

Dr. Magdalena Piekutowska
Prof. Dr. Gniewko Niedbała
Dr. Tomasz Wojciechowski
Dr. Mohsen Niazian
Guest Editors

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