A linked modelling framework to explore interactions among climate, soil water, and land use decisions in the Argentine Pampas

García, Guillermo; García, Pablo E.; Rovere, Santiago L.; Bert, Federico; Schmidt, Federico Martín; Menéndez, Ángel N.; Nosetto, Marcelo D.; Verdin, A.; Rajagopalan, Balaji; Arora, Poonam; Podestá, Guillermo

doi:10.1016/j.envsoft.2018.10.013

Cited by 19 publications

(4 citation statements)

References 70 publications

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“…These fields should be sown in early sowing dates, increasing yields up to 1742 kg ha −1 if rainfall levels are lower than average. Management tools to decide sowing date based on the availability of a water table within a specific depth are currently available, like groundwater elevation maps (Nosetto et al, 2009), closed phreatimetric nodes (Aragón et al, 2011), or software tools (García et al, 2018). Similarly, only a small proportion of farmers applied fungicides in LS, another management option that can help increase yields.…”

Section: Regional Implications For Reducing Maize Yield Gapsmentioning

confidence: 99%

Management options for reducing maize yield gaps in contrasting sowing dates

Vitantonio‐Mazzini

Borrás

Garibaldi

et al. 2020

Field Crops Research

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Optimizing grain production implies defining the best management practices for a set of particular environments. Argentinean farmers in the central temperate region are sowing maize at two contrasting sowing dates (September to October and December), exposing their crops to very different growing environments. We tested the influence of management and environmental variables affecting maize yield at early (ES) or late (LS) sowings. Our objectives were to (i) determine the most relevant management and environmental yield predictors for ES and LS, (ii) quantify the magnitude of their effects, and (iii) explore potential yield increments after optimizing crop management within each sowing. We conducted 91 on-farm multi-environment trials during six years (2010)(2011)(2012)(2013)(2014)(2015)(2016) around the central temperate region, and analyzed 13 management and environmental variables.The most relevant environmental predictors (relative importance > 0.50) for both sowing dates included presence of a water table at sowing, rainfall during the crop cycle, and their interactions. Presence of a water table had a positive or negative effect for ES or LS, respectively. Management yield predictors varied depending on the sowing date. Stand density, N and S availability were important yield predictors at ES, while fungicide use, soil P, and N availability were the most relevant ones at LS. Farmers can increase yield at each sowing date by optimizing these management practices. Optimizing stand density and N availability in ES can have a ∼3,053 kg ha −1 effect, while fungicide use in LS can increase yield by ∼1040 kg ha −1 . Determining the adequate sowing date based on the presence of a water table at sowing can have a ∼1000 kg ha −1 effect. Our results described specific management options for reducing yield gaps and optimize maize production across contrasting sowing dates.Argentinean farmers in the central temperate region are sowing their maize crops at two contrasting dates. Early sowing dates (ES) take place during September-October, while late sowing dates (LS) take place around December. While ES is traditional for the region, LS became important more recently following the introduction of insect resistant (i.e., Bt) corn in late 1990s. These contrasting sowing dates expose the crop to different scenarios in terms of temperature, solar radiation, and water availability (Maddonni, 2012). Late sowing dates locate the critical flowering period for yield definition (Andrade et al., 1999) under conditions of higher rainfall probability, lower evaporative

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Section: Regional Implications For Reducing Maize Yield Gapsmentioning

confidence: 99%

Management options for reducing maize yield gaps in contrasting sowing dates

Vitantonio‐Mazzini

Borrás

Garibaldi

et al. 2020

Field Crops Research

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“…Desde 1990 hasta el final del período simulado, esas diferencias van disminuyendo hasta casi desaparecer en los últimos años. Este nuevo cambio en la dinámica de las napas obedece a dos razones: primero, la introducción del doble cultivo, tal como se explicó en la sección 2.2, que ha generado un aumento de la evapotranspiración total anual; y segundo, que la presencia de napas freáticas tan cerca de la superficie hace que se minimicen las diferencias de evapotranspiración total anual entre los cultivos y las pasturas [22].…”

Section: Dinámica De Niveles Freáticos Y Caudalesunclassified

“…Este trabajo se enmarca bajo el proyecto "From Farm Management to Governance of Landscapes: Climate, Water, and Land-Use Decisions in the Argentine Pampas" financiado por la National Science Foundation (NSF), donde previamente se estudió la posibilidad de manejar la profundidad de napa mediante el uso del suelo (a escala de cuenca y a escala de campo), la eficiencia de ese manejo bajo diferentes climas y si las decisiones de un productor afectan el nivel freático de campos aledaños [21]. En este proyecto se ha desarrollado un modelo integrado (que incluye un generador climático, un modelo hidrológico y un modelo de agentes) con el cual se pretende evaluar las implicancias que tendrían desde el punto de vista hidrológico y económico, cambios en el precio de los diferentes "commodities" o productos, cambios en la política de impuestos y en diferentes tipos de rotación de cultivos [22].…”

Section: Introductionunclassified

Influencia de los cambios en el uso del suelo y la precipitación sobre la dinámica hídrica de una cuenca de llanura extensa. Caso de estudio: Cuenca del Río Salado, Buenos Aires, Argentina

García

Badano

Menéndez

et al. 2018

Ribagua

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“…One of the most popular approaches to hydro-economic modelling in water resources management is what we herein refer to as 'the engineering-based approach'. This approach involves models that are based on a detailed representation of the water component, e.g., nodelink networks, and a simplified representation of the economic component, e.g., an estimation of changes in sectoral production multiplied by an average market price (e.g., Cai et al, 2003;Jenkins et al, 2004;Medellín-Azuara et al, 2007;Harou et al, 2010;Razavi et al, 2013;Asadzadeh et al, 2014;Graveline et al, 2014;Ward, 2014;Esteve et al, 2015;Nguyen et al, 2016;Basheer et al, 2018;Kahil et al, 2018;Mirchi et al, 2018;Amjath-Babua et al, 2019;García et al, 2019;Geressu and Harou, 2019;Do et al, 2020). Such representations of the economy are referred to as 'partial equilibrium' models because they, generally speaking, only account for a portion of the economy like one or a few sectors, such as agriculture or hydropower, without considering interdependencies between sectors or regions within a river basin .…”

Section: Introductionmentioning

confidence: 99%