Modeling soil organic carbon and carbon dioxide emissions in different tillage systems supported by precision agriculture technologies under current climatic conditions

Cillis, Donato; Maestrini, Bernardo; Pezzuolo, Andrea; Marinello, Francesco; Sartori, Luigi

doi:10.1016/j.still.2018.06.001

Cited by 32 publications

(17 citation statements)

References 43 publications

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“…It has been validated to simulate maize and pigeonpea aboveground biomass and grain yield under the humid subtropical and the tropical savanna climate in Malawi (Liu and Basso 2017). Other SALUS model testing includes grain yield (Basso and Ritchie 2015), soil water content (Basso et al 2010;Hamilton et al 2015), soil carbon (Cillis et al 2018;Pezzuolo et al 2017), and soil nitrate and nitrate leaching (Basso et al 2016a;Giola et al 2012) under the Mediterranean and humid continental climates. For this study, we tested SALUS model to evaluate its capability to reproduce interannual maize grain yield at regional level ( Fig.…”

Section: Description Of the Salus Crop Modelmentioning

confidence: 99%

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Liu

Basso

2020

Food Sec.

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Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80's identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers' fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r 2 values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14-77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2-5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large.

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Section: Description Of the Salus Crop Modelmentioning

confidence: 99%

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Liu

Basso

2020

Food Sec.

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“…Tillage operations have a great impact on topsoil in terms of aggregate size and crop residue cover, which plays an important role in sustainable agriculture. A minimum amount of residue is needed to protect soil erosion, reduce greenhouse gas emissions and maintain soil carbon level [5][6][7][8]. The impact of tillage operations on topsoil depend on tillage operational depth and speed, soil characteristics, initial amount of residues, and type of used equipment [9,10].…”

Section: Introductionmentioning

confidence: 99%

Assessing Topsoil Movement in Rotary Harrowing Process by RFID (Radio-Frequency Identification) Technique

et al. 2019

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Harrowing is a process that reduces the size of soil clods and prepares the field for seeding. Rotary harrows are a common piece of equipment in North Italy that consists of teeth rotating around a vertical axis with a processing depth of 5–15 cm. In this study, the topsoil movement in terms of distance and direction were estimated at different rotary harrow working conditions. A total of eight tests was performed using two forward speeds of 1 and 3 km/h, two working depths of 6 and 10 cm and two levelling bar positions of 0 and 10 cm from the ground. In order to simulate and follow topsoil movement, Radio-Frequency Identification (RFID) tags were inserted into cork stoppers and distributed in a regular pattern over the soil. Tags were distributed in six lines along the working width and repeated in three rows for each test: a total number of 144 tags was tracked. Results showed that there were no significant differences between the performed tests, on the other hand the reported tests highlight the effectiveness of the RFID monitoring approach.

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“…A cover crop was sown after the main crop to keep a permanent soil cover. The cover crop was established on plots undergoing conservation tillage systems, which was devitalized with the soil cultivation in strip and minimum tillage or with the use of herbicides in no-tillage [34,35]. The case study aims to assess the qualitative water footprint of the processes during different crop seasons and different soil tillage systems under precision (VRA) and tradition (URA) farming that are described in the experimentation pattern shown in Figure 1b.…”

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confidence: 99%

“…The homogenous zones are identified using the Salus model in a map of prescription as homogenous areas of soil spatial variability of soil productive potential [13,36]. The Salus model is designed to simulate crop production under different soil management, simulating the dynamics of nutrients with different atmospheric and soil conditions [34]. Using the Soil Texture Triangle Hydraulic Properties Calculator from Saxton et al (1986) [37], three different soil textures were identified (Table 2): Sandy Loam Soil (Zones A and B), Loam Soil (Zone C), and Clay Loam Soil (Zone D).…”

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Evaluation of the Grey Water Footprint Comparing the Indirect Effects of Different Agricultural Practices

et al. 2018

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Increasing global food demand and economic growth result in increasing competition over scarce freshwater resources, worsened by climate change and pollution. The agricultural sector has the largest share in the water footprint of humanity. While most studies focus on estimating water footprints (WFs) of crops through modeling, there are only few experimental field studies. The current work aims to understand the effect of supposedly better agricultural practices, particularly precision agriculture (variable rate application of fertilizers and pesticides) and conservation agriculture (minimum, strip, or no-tillage), on water deterioration and water pollution. We analyzed the results from an experimental field study in the northeast of Italy, in which four different crops are grown across three years of crops rotation. We compared minimum, strip, and no-tillage systems undergoing variable to uniform rate application. Grey WFs are assessed based on a field dataset using yield maps data, soil texture, and crop operations field. Leaching and associated grey WFs are assessed based on application rates and various environmental factors. Yields are measured in the field and recorded in a precision map. The results illustrate how precision agriculture combined with soil conservation tillage systems can reduce the grey water footprint by the 10%. We assessed the grey Water Footprint for all the field operation processes during the three-year crop rotation.

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Modeling soil organic carbon and carbon dioxide emissions in different tillage systems supported by precision agriculture technologies under current climatic conditions

Cited by 32 publications

References 43 publications

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Assessing Topsoil Movement in Rotary Harrowing Process by RFID (Radio-Frequency Identification) Technique

Evaluation of the Grey Water Footprint Comparing the Indirect Effects of Different Agricultural Practices

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