Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast

Rizzo, Gonzalo; Mazzilli, Sebastián R.; Ernst, Oswaldo; Baethgen, Walter E.; Berger, Andrés G.

doi:10.1016/j.agsy.2021.103331

Cited by 12 publications

(8 citation statements)

References 58 publications

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“…Double cropped soybean at the Moscoso‐Molina site, yielded 1,995 kg ha –1 and was in the range of estimated actual yield for the region (2,000 kg ha −1 ) but below the values of water‐limited yield (3,400 kg ha −1 ) and potential yield (5,700 kg ha −1 ) (Rizzo et al., 2021). These results are in agreement with the low water regime that occurred in this season (Rizzo et al., 2022), and these water limitations may have masked the residual effect of the treatments (Calviño & Sadras, 1999; Vitantonio‐Mazzini et al., 2021). A more limiting environment was experienced by the double‐cropped maize at the Rosello‐Dalmás site, where only 390 mm of rainfall occurred during the crop cycle, reaching an average yield of 10% of the potential values reported for the region (Hayashi & Water, 2021).…”

Section: Discussionsupporting

confidence: 85%

Use of sludge from pulp mills in extensive crops: An assessment at a semi‐commercial scale in central South America

Mazzilli¹,

Barrios²,

Ernst³

2022

Agrosystems Geosci & Env

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Land under agriculture has increased during the last 20 yr in the South American Pampas. Agricultural systems use mostly finite imported sources of P, so the use of local recycled P sources is an alternative. In Uruguay, two current events are relevant to this matter: the increase in the crop area of Brassica carinata (biofuels production) and the presence of large pulp mills. The working hypothesis was that pulp mill sludge applied prior to sowing carinata crops will substitute the use of inorganic fertilizers, and that the combination of sludge and inorganic fertilizers will increase grain yield. Two on-farm experiments were conducted, in which carinata was sown during the 2019 winter season and followed after harvest by soybean sown at one site and maize at another. At each site, six treatment strips (30 m wide × 400 m long) were established in a control-plot design. The treatments corresponded to two sludge types (biosludge and mixed sludge), combined with a traditional N, P, K, S chemical fertilizer (29 kg P ha -1 ), compared with a control treatment (not fertilized). Carinata yields results do not support the proposed hypothesis, because the yields of the sludge treatments were lower than the chemical fertilization treatments. The limiting factor for the crops was associated with N supply, as the nitrogen nutrition index showed low values. Sludge application did not result in changes in summer crop yields and soil chemical properties. Future studies should focus on the sustained use of combined fertilizer sources over time.

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Section: Discussionsupporting

confidence: 85%

Use of sludge from pulp mills in extensive crops: An assessment at a semi‐commercial scale in central South America

Mazzilli¹,

Barrios²,

Ernst³

2022

Agrosystems Geosci & Env

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“…The use of categorical recommendations (i.e. low or high) may stem partially from the categorization of ENSO phases that result in categorical expectations of yield and consequently crop management [25,67]. Probabilistic models can act as triggers for conversations among stakeholders on the possible scenarios and their associated risks.…”

Section: Discussionmentioning

confidence: 99%

“…machine learning algorithms). Rizzo et al [25] integrated sub-seasonal predictions into recommendation models, but without a Bayesian framework and probabilistic forecasts. In this study, we develop a novel application of seasonal climate predictions to inform optimal use of inputs within a Bayesian statistical framework.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

Lacasa

Messina

Ciampitti

2023

Environ. Res. Lett.

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Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant, and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m-2), thus this variable could be optimized conditional to the seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density (herein termed as yield-density) relationship as a function of weather variables, (ii) evaluate the predictive performance and analyze the sources of uncertainty, and (iii) provide probabilistic forecasts for predicting the economic optimum plant density (EOPD). We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model provides crop management recommendations by accounting for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield-density trials performed during the 2010-2019 period across 7 states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute (IRI) were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield-density relationship were weather anomalies, especially months with above-normal temperatures. Improvements on climate forecasting may also improve precision, as the coefficient of determination (R2) increased from 0.26 to 0.32 when weather forecasts were correct. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

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“…Increased knowledge of El Niño–Southern Oscillation (ENSO) teleconnection in the 1980s and 1990s contributed to improving seasonal climate forecasting skills with a few months of lead time (Goddard et al., 2001; O'Lenic et al., 2008). Compared to weather forecasts, seasonal climate forecasts (SCFs) have relatively low prediction skills (White et al., 2017) but are found helpful for agricultural decisions that need longer lead times, particularly in ENSO‐sensitive regions (Brown et al., 2018; Hansen, 2005; Rizzo et al., 2022). International climate communities have continuously made efforts to produce accurate and seamless weather and climate predictions for weather, subseasonal, and decadal timescales (Merryfield et al., 2020).…”

Section: Application Of Crop Models To Deal With Interannual Climate ...mentioning

confidence: 99%

“…Rizzo et al. (2022) evaluated the skills of operational tercile SCFs from the International Research Institute of Climate and Society (IRI) and values in determining best management practices (i.e., optimal planting dates and soybean varieties in different maturity groups) and in predicting soybean yields for below‐ and above‐normal seasons in Uruguay.…”

Section: Application Of Crop Models To Deal With Interannual Climate ...mentioning

confidence: 99%

The role of crop simulation modeling in assessing potential climate change impacts

Timlin,

Paff,

Han

2024

Agrosystems Geosci & Env

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Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed.

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Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast

Cited by 12 publications

References 58 publications

Use of sludge from pulp mills in extensive crops: An assessment at a semi‐commercial scale in central South America

Use of sludge from pulp mills in extensive crops: An assessment at a semi‐commercial scale in central South America

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

The role of crop simulation modeling in assessing potential climate change impacts

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