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Article: IC-FAR: Linking Long Term Observatories with Crop Systems Modeling For a better understanding of Climate Change Impact, and Adaptation Strategies for Italian Cropping Systems

Basso, Bruno

FACCE MACSUR Reports, 3(Supplement):CP-86

2014

Abstract: The IC-FAR project (2013-2016), funded by the Italian ministry of University, Research and Education, aims to use datasets from 16 Italian long term agronomic experiments (LTEs) to assess the reliability of different cropping system models over a range ... ...

Abstract	The IC-FAR project (2013-2016), funded by the Italian ministry of University, Research and Education, aims to use datasets from 16 Italian long term agronomic experiments (LTEs) to assess the reliability of different cropping system models over a range of Mediterranean environments and cropping systems. The selected models will be used for scenario and uncertainty analyses vs near-future climate change. The LTEs are located in seven sites: Turin, Padua, Bologna, Ancona, Pisa, Perugia, Foggia. The project’s is linked to international projects such as MACSUR, AgMIP, ANAEE, ESFRI and GRA, and has model developer teams as associate partners. IC-FAR is structured in five WPs. WP1 is focused on building a common dataset and sampling protocols. The field data will be implemented in the WP2 to calibrate, validate and assess the performances of different models across Italian environments. An uncertainty analysis will be performed in relation to the model types, cropping system typologies and climate scenarios (WP3). WP4 and WP5 are focused on capacity building on modeling and on dissemination, including networking with other European LTE platforms (WP4), and to the project coordination (WP5).The next step of IC-FAR will be the design and realization of a special issue summarizing a selection of the most important results from the LTEs, that will be the starting point towards the full implementation of the data sharing policy of this project.
Language	English
Document type	Article
DOI	10.4126/FRL01-006413498
Database	Repository for Life Sciences

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Article ; Online: Precision conservation for a changing climate.

Basso, Bruno

Nature food

2021 Volume 2, Issue 5, Page(s) 322–323

Language	English
Publishing date	2021-05-20
Publishing country	England
Document type	Journal Article
ISSN	2662-1355
ISSN (online)	2662-1355
DOI	10.1038/s43016-021-00283-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Global Research Alliance on Greenhouse Gases - benchmark and ensemble crop and grassland model estimates

Sándor, Renáta / Basso, Bruno / Bellocchi, Gianni / Brilli, Lorenzo / DORO, Luca

FACCE MACSUR Reports, 8:SP8-14

2016

Language	English
Document type	Article
DOI	10.4126/FRL01-006413190
Database	Repository for Life Sciences

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Article: Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models

Shuai, Guanyuan / Basso, Bruno

Remote sensing of environment. 2022 Apr., v. 272

2022

Abstract: In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this ... ...

Abstract	In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.
Keywords	Landsat ; case studies ; chlorophyll ; climate ; corn ; crop models ; crop yield ; data collection ; digital elevation models ; drought ; environment ; prediction ; remote sensing ; soil ; stakeholders ; vegetation index ; yield forecasting ; Illinois ; Indiana ; Iowa ; Michigan
Language	English
Dates of publication	2022-04
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
DOI	10.1016/j.rse.2022.112938
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Subfield crop yields and temporal stability in thousands of US Midwest fields.

Maestrini, Bernardo / Basso, Bruno

Precision agriculture

2021 Volume 22, Issue 6, Page(s) 1749–1767

Abstract: Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed ... ...

Abstract	Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha Supplementary information: The online version contains supplementary material available at 10.1007/s11119-021-09810-1.
Language	English
Publishing date	2021-05-08
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2016333-2
ISSN	1573-1618 ; 1385-2256
ISSN (online)	1573-1618
ISSN	1385-2256
DOI	10.1007/s11119-021-09810-1
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Article: Subfield crop yields and temporal stability in thousands of US Midwest fields

Maestrini, Bernardo / Basso, Bruno

Precision agriculture. 2021 Dec., v. 22, no. 6

2021

Abstract	Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha⁻¹ for maize, 0.39 Mg ha⁻¹ for cotton, 0.34 Mg ha⁻¹ for soybean, and 0.59 Mg ha⁻¹ for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.
Keywords	algorithms ; corn ; cotton ; data collection ; soybeans ; standard deviation ; temporal variation ; wheat ; Midwestern United States
Language	English
Dates of publication	2021-12
Size	p. 1749-1767.
Publishing place	Springer US
Document type	Article
ZDB-ID	1482656-2
ISSN	1385-2256
ISSN	1385-2256
DOI	10.1007/s11119-021-09810-1
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields.

Fowler, Ames / Basso, Bruno / Maureira, Fidel / Millar, Neville / Ulbrich, Ruben / Brinton, William F

Scientific reports

2024 Volume 14, Issue 1, Page(s) 465

Abstract: Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management ... ...

Abstract	Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers' fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ.
Language	English
Publishing date	2024-01-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51155-y
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Article ; Online: A simple soil mass correction for a more accurate determination of soil carbon stock changes.

Fowler, Ames F / Basso, Bruno / Millar, Neville / Brinton, William F

Scientific reports

2023 Volume 13, Issue 1, Page(s) 2242

Abstract: Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and measuring stock change stipulates fixed depth sampling to ... ...

Abstract	Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and measuring stock change stipulates fixed depth sampling to at least 30 cm. The tendency of bulk density (BD) to decrease with decreasing disturbance and increasing SOC concentration and the assumption of constant SOC and BD within this depth profile promotes error in the estimates of SOC stock. A hypothetical but realistic change in BD from 1.5 to 1.1 g cm
Language	English
Publishing date	2023-02-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-29289-2
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Article ; Online: Impacts of climate variability and adaptation strategies on crop yields and soil organic carbon in the US Midwest.

Liu, Lin / Basso, Bruno

PloS one

2020 Volume 15, Issue 1, Page(s) e0225433

Abstract: Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon ( ... ...

Abstract	Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10-22% for maize and 5-13% for wheat) rather than in early season (5-17% for maize and 2-18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4-2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses.
MeSH term(s)	Acclimatization/physiology ; Adaptation, Physiological ; Agriculture ; Carbon/metabolism ; Climate Change ; Crops, Agricultural/growth & development ; Droughts ; Edible Grain ; Humans ; Midwestern United States ; Seasons ; Soil ; Glycine max/growth & development ; Triticum/growth & development ; Zea mays/growth & development
Chemical Substances	Soil ; Carbon (7440-44-0)
Language	English
Publishing date	2020-01-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0225433
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Liu, Lin / Basso, Bruno

Food security. 2020 June, v. 12, no. 3

2020

Abstract: 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 ... ...

Abstract	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 (r² 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.
Keywords	administrative management ; at-risk population ; corn ; crop models ; fields ; food security ; reliability ; simulation models ; small-scale farming ; surveys ; uncertainty ; yields ; Tanzania
Language	English
Dates of publication	2020-06
Size	p. 537-548.
Publishing place	Springer Netherlands
Document type	Article
Note	NAL-light
ZDB-ID	2486755-X
ISSN	1876-4525 ; 1876-4517
ISSN (online)	1876-4525
ISSN	1876-4517
DOI	10.1007/s12571-020-01020-3
Database	NAL-Catalogue (AGRICOLA)

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