Potential for crop production increase in Argentina through closure of existing yield gaps

Merlos, Fernando Aramburu; Monzón, Juan Pablo; Mercau, Jorge L.; Taboada, Miguel Ángel; Andrade, Fernando H.; Hall, A. J.; Jobbágy, Estéban G.; Cassman, Kenneth G.; Grassini, Patricio

doi:10.1016/j.fcr.2015.10.001

Cited by 159 publications

(74 citation statements)

References 49 publications

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“…YG CM showed to be linked with YG WD in Castro and Mamborê as also observed by Merlos et al (2015). These authors found that the magnitude of soya bean YG CM was higher during wet years than during dry years, as well as for wheat and maize in Argentina.…”

Section: Discussionsupporting

confidence: 68%

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Teixeira

Battisti

Sentelhas

et al. 2019

J Agronomy Crop Science

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Soya bean yield gap can be caused by different factors resulting in uncertainties when the objective is to use such information for farm decision‐making and reference yield determination. Thus, this study aimed to quantify the soya bean yield gap for four sites, located in Southern and Midwestern Brazil, as well as the uncertainties of that related to cultivars, sowing dates, soil types and reference yields. The crop simulation model DSSAT‐CSM‐CROPGRO‐Soybean was calibrated for cultivars with similar maturity groups, based on the data obtained from the best farmers at the county level. The yield gap by water deficit (YGWD) was obtained through the difference between potential and attainable yields, and that one caused by sub‐optimum crop management (YGCM) by subtracting actual yield of each county, obtained from official statistics between 1989/90 and 2014/15 growing seasons, from the estimated attainable yield. The yield was simulated using four sowing dates, three soil types and two soya bean maturity groups by county. The reference yield uncertainty was quantified using yield reference from crop model and regional winners of the soya bean yield context, conducted by CESB (Brazilian Soybean Strategic Committee), for the growing seasons from 2013/14 to 2015/16. The crop model showed a good agreement between measured and simulated crop development and growth using calibration by maturity group, with low root mean square error (347 kg/ha). Southern sites had a mean YGWD of 1,047 kg/ha, while in the Midwest, it was lower than 100 kg/ha. The YGCM was 1,067, 528, 984 and 848 kg/ha, respectively, for Castro, PR, Mamborê, PR, Montividiu, GO and Primavera do Leste, MT, representing the opportunity for yield gain when having the best farmers as reference. The maturity groups, sowing dates and soil types showed to be an important source of uncertainty for yield gap determination, being recommended to investigate the farms in detail for an appropriate quantification. The reference yield showed expressive uncertainties, with some farmers presenting conditions to increase their soya bean yields by more than 3,000 kg/ha, when considering as reference the yields obtained by the winners’ farmers. These results show that uncertainties must be reduced when assessing farm yield gaps, in order to ensure that expected rate of soya bean yield growth could be reached by adopting the same technologies from CESB winners and best farmers in the county as a reference.

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Section: Discussionsupporting

confidence: 68%

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Teixeira

Battisti

Sentelhas

et al. 2019

J Agronomy Crop Science

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“…These values are far from the proposed 30%. Drought stress environments yielding <30% from well‐watered situations would represent environments that are not realistic for soybean production in Central Argentina (Aramburu Merlos et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…Once planting date is determined, there is no yield penalty for choosing a yield potential strategy for this region. In this sense, agronomic management has the same impact under different climatic scenarios, and therefore is important in years with high or low yield expectations (Aramburu Merlos et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…In Argentina, despite abundant water availability random periods of water stress can occur associated with El Niño‐Southern Oscillation phenomenon (Ropelewski & Halpert, ). Variability in rainfall causes substantial variation in soybean yield across years (Aramburu Merlos et al., ; Podestá, Messina, Grondona, & Magrin, ; Sinclair, Salado‐Navarro, Morandi, Bodrero, & Martignone, ). In general, water deficits during reproductive stages impact soybean yield more than deficits during vegetative ones (Ashley & Ethridge, ; Brown, Caviness, & Brown, ; Doss, Pearson, & Rogers, ).…”

Section: Introductionmentioning

confidence: 99%

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Exploring soybean management options for environments with contrasting water availability

Mauro

Borrás

Rugeroni³

et al. 2019

J Agronomy Crop Science

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Soybean is commonly cultivated under rainfed conditions being water availability the main constraint. We evaluated the performance of different managements under contrasting water availability to test possible trade‐offs among managements, and to determine physiological variables explaining these yield differences. Four treatments were designed through specific combinations of cultivar, row spacing and stand density. They were classified as stress tolerance or yield potential strategies and were evaluated under two contrasting water availability treatments. Treatments ranged from 349 to 954 mm total water availability. Water stress treatments yielded 72% and 59% of the well‐watered treatment each year, similar to frequent soybean water stress levels for our production region. Management treatments showed significant yield differences (p < 0.05), but the management × water availability interaction did not (p = 0.42). No management option helped reduce negative water stress effects. Highest yields were achieved using 0.25 m row spacing, a stand density of 60 pl per m2, and a high yield potential genotype. Yield variations were explained by differences in harvested seeds per unit area (R2 = 0.75; p < 0.001) and total N uptake at maturity (R2 = 0.93; p < 0.001) across environments. Because management strategies specifically tailored to cope with water shortages showed limited value, farmers need to target yield potential management options.

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“…The gap between Yp and Yr, called the ‘water limitation index’ (WLI) (equation (4)), provides a measure of the degree to which crops are limited by water 59 . …”

Section: Methodsmentioning

confidence: 99%

A Model-Based Estimate of Regional Wheat Yield Gaps and Water Use Efficiency in Main Winter Wheat Production Regions of China

Liu

Cao

et al. 2017

Sci Rep

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Wheat production is of great importance for national food security and is greatly influenced by the spatial variation of climatic variables, soils, cultivars, etc. This study used WheatGrow and CERES-Wheat models integrated with a GIS to estimate winter wheat productivity, yield gap and water use in the main wheat production regions of China. The results showed that the potential wheat yield gradually increased from south to north and from west to east, with a spatial distribution consistent with the accumulated hours of sunshine. The gap between potential and actual yield varied from 382 to 7515 kg ha−1, with the highest values in Shanxi, Gansu and Shaanxi provinces and the lowest values in Sichuan province. The rainfed yield decreased gradually from south to north, roughly following the pattern of the ratio of accumulated precipitation to accumulated potential evapotranspiration. Under the scenario of autoirrigation, relatively high irrigation water use efficiency was found in western Shandong and southern Sichuan, as well as in northern Henan, Shanxi and Shaanxi. Furthermore, the limiting factors were analysed, and effective measures were suggested for improving regional winter wheat productivity. These results can be helpful for national policy making and water redistribution for agricultural production in China.

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Potential for crop production increase in Argentina through closure of existing yield gaps

Cited by 159 publications

References 49 publications

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Exploring soybean management options for environments with contrasting water availability

A Model-Based Estimate of Regional Wheat Yield Gaps and Water Use Efficiency in Main Winter Wheat Production Regions of China

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