Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation

Basso, Bruno; Bertocco, M.; Sartori, Luigi; Martin, E. C.

doi:10.1016/j.eja.2006.08.008

Cited by 98 publications

(56 citation statements)

References 26 publications

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“…Data were all measured on site (Table 3) and soil hydraulic properties were calculated using the procedure suggested by Ritchie et al (1999). The SALUS model has been widely validated for maize yields and nitrogen leaching under variable N fertilizer rates in the study area, as reported in Basso et al (2007Basso et al ( , 2010Basso et al ( , 2011aBasso et al ( , 2011bBasso et al ( , 2012; Bertocco et al (2008) and Pezzuolo et al (2014).…”

Section: Crop Growth Model and Simulation Scenariosmentioning

confidence: 99%

“…FPI is an index ranging from 0 to 1, which values approaching 0 indicate distinct classes with low sharing, while values near 1 indicate non-distinct classes with a large degree of property sharing. More details about the procedure can be found in Chiericati et al (2007) and Basso et al (2007).…”

Section: Methodology For Identification Of Uniform Management Zonesmentioning

confidence: 99%

“…The potential benefits of SSM strategies are greatly dependent on the how accurately such variability can be assessed. Many authors have proposed guidelines for the delineation of management zones which can be defined as areas within a field that are homogeneous with regard to yield limiting factors (Mulla, 1991, Ferguson et al, 2004, Schepers et al, 2004, Chang et al, 2004Basso et al, 2007;. Pierce and Nowak (1999) highlighted how temporal and spatial variability of soil N has to be taken into account for successful SSM of N fertilization.…”

Section: Introductionmentioning

confidence: 99%

“…Plant response to variable management levels is highly dependent upon the weather that occurs during a given growing season (Basso et al, 2007;Basso et al, 2013;Dumont et al, 2014Dumont et al, , 2015a. For example, crop response to N in rainfed environments may be high when water is available, or low when the soil water content is limited (Basso et al, 2011a).…”

Section: Introductionmentioning

confidence: 99%

“…However their application in PA can be limited because simulations cannot be performed everywhere in a field given that the availability of detailed (soil and crop data) inputs is limited and the costs are prohibitive. Basso et al (2007) used a more balanced approach to study spatial and temporal variability of crop behavior in a field when they applied a crop simulation model for SSM. They considered temporal stability and spatial variability of measured yield maps to delineate stable or unstable spatial patterns and identify zones of similar crop performance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Basso

Dumont

Cammarano

et al. 2016

Science of The Total Environment

Self Cite

139

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Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha− 1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha− 1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching

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Section: Crop Growth Model and Simulation Scenariosmentioning

confidence: 99%

Section: Methodology For Identification Of Uniform Management Zonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Basso

Dumont

Cammarano

et al. 2016

Science of The Total Environment

Self Cite

139

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The impact of El Niño Southern Oscillation phases on off‐season maize yield for a subtropical region of Brazil

Soler

Sentelhas

Hoogenboom

2009

Intl Journal of Climatology

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ABSTRACT:In recent years, maize has become one of the main alternative crops for the autumn-winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Niño Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of São Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Niño (warm sea surface temperature anomalies in the Pacific), La Niña (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Niño, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making.

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A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

Rossini

Ciampitti

Hefley

et al. 2021

Vadose Zone Journal

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Soil moisture information is a key variable for guiding in-season management decisions in rainfed and irrigated agricultural systems. However, methods for deciding the number and location of soil moisture sensors (SMS) per field still remain poorly explored in the scientific literature. The goal of this study was to evaluate a quantitative framework based on soil moisture-based management zones (MZs) to determine the minimum number and tentative deployment location of SMS. Multiple spatially intensive (n > 100 observations) surveys of near-surface (0-12 cm) soil moisture were conducted during the fallow periods and early growing seasons of 2017, 2018, and 2019 on three agricultural fields using a calibrated handheld soil water reflectometer. The fuzzy C-means (FCM) clustering method was used to delineate MZs based on the soil moisture surveys, and the silhouette clustering evaluation method was used to identify the optimal number of MZs per field. Then, a sensor location index that considered the distance to the MZ boundaries and the FCM membership grade was developed to identify the tentative optimal deployment location of SMS. The proposed method effectively identified field areas with distinct soil moisture regimes and revealed the complex soil moisture spatial patterns that were not captured with elevation or soil texture alone. Dividing the fields using soil moisture-based MZ reduced the intrazone soil moisture spatial variability by about 50% compared with that of the entire field. In the three studied fields, a total of two SMS were sufficient to capture the salient soil moisture spatial regimes.

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Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation

Cited by 98 publications

References 26 publications

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

The impact of El Niño Southern Oscillation phases on off‐season maize yield for a subtropical region of Brazil

A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

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