Seesaw production and pollution associated with COVID-19-like supply chain disruptions in an agroecosystem model, Cycles Data

Sudden global disruptions in agricultural commodities demand or input supply have local immediate effects on the economy and the environment. Recognizing these patterns can help society and agricultural stakeholders prepare and react to these disruptions. This research addresses the use of agricultural decision support systems (DSS) to quickly assess the impacts of disruptions like the COVID-19 pandemic on agroecosystem production output and pollution. We diagnosed the effect of assumed labor and agrochemical supply disruptions on grain and forage yield and nitrogen (N) pollution using the agroecosystem model Cycles. As a demonstration, we consider Sugar Valley, a dairy production area in Pennsylvania. Relevant simulations were developed using a generic supply-demand framework that suggests a seesaw effect on production and N pollution derived from changes in annual crop management. First, as in the early stages of COVID-19, demand shocks decrease production. Thereafter, input supply scarcity (e.g., agrochemicals) is followed by pollution dips. However, N pollution can increase sharply and reach above pre-disruption levels if demand recovers and there are no limitations to N fertilizer supply. With simulated supply and demand disruptions inspired by those observed during the COVID-19 pandemic, we estimated corn yield and N pollution decreases of 34 and 30% when a disruption suddenly decreases commodity demand and subsequently limits agrochemical supply. Thereafter, when demand recovery favors increased yield, pollution overshoots the baseline by about 10%. Our modeling suggests that disruptions that prevent timely weed control are most critical for Pennsylvanian agroecosystems with dairy production, revealing the vulnerability of systems dependent on agrochemicals, a vulnerability that extends to other biotic stresses. We suggest that diversification through crop rotations can reduce regional dependence on single agrochemical inputs and provide resilience to external shocks, for example by reducing N fertilizer requirements when cereals follow legumes. In conclusion, we show that i) DSS for agroecosystem analysis can be deployed quickly, at large scale, and on a field-by-field basis, allowing for agile emergency assessments and response, and ii) agroecosystems that are efficient at the cost of low redundancy and high-dependence on single inputs have sharper, unbuffered production and pollution responses to demand and supply shocks.immediate effects on the economy and the environment. Recognizing these patterns can help society and agricultural stakeholders prepare and react to these disruptions. This research addresses the use of agricultural decision support systems (DSS) to quickly assess the impacts of disruptions like the COVID-19 pandemic on agroecosystem production output and pollution. We diagnosed the effect of assumed labor and agrochemical supply disruptions on grain and forage yield and nitrogen (N) pollution using the agroecosystem model Cycles. As a demonstration, we consider Sugar Valley, a dairy production area in Pennsylvania. Relevant simulations were developed using a generic supply-demand framework that suggests a seesaw effect on production and N pollution derived from changes in annual crop management. First, as in the early stages of COVID-19, demand shocks decrease production. Thereafter, input supply scarcity (e.g., agrochemicals) is followed by pollution dips. However, N pollution can increase sharply and reach above pre-disruption levels if demand recovers and there are no limitations to N fertilizer supply. With simulated supply and demand disruptions inspired by those observed during the COVID-19 pandemic, we estimated corn yield and N pollution decreases of 34 and 30% when a disruption suddenly decreases commodity demand and subsequently limits agrochemical supply. Thereafter, when demand recovery favors increased yield, pollution overshoots the baseline by about 10%. Our modeling suggests that disruptions that prevent timely weed control are most critical for Pennsylvanian agroecosystems with dairy production, revealing the vulnerability of systems dependent on agrochemicals, a vulnerability that extends to other biotic stresses. We suggest that diversification through crop rotations can reduce regional dependence on single agrochemical inputs and provide resilience to external shocks, for example by reducing N fertilizer requirements when cereals follow legumes. In conclusion, we show that i) DSS for agroecosystem analysis can be deployed quickly, at large scale, and on a field-by-field basis, allowing for agile emergency assessments and response, and ii) agroecosystems that are efficient at the cost of low redundancy and high-dependence on single inputs have sharper, unbuffered production and pollution responses to demand and supply shocks.