Did someone say “farmer-centric”? Digital tools for spatially distributed on-farm experimentation

Bramley, R. G. V.; Song, Xinxin; Colaço, André Freitas; Evans, Katherine J; Cook, Simón

doi:10.1007/s13593-022-00836-x

Cited by 9 publications

(5 citation statements)

References 66 publications

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“…Even with automation, the entire analysis needs to be simple for farmers to carry out while providing robust and relevant metrics of treatment effects along the length of the trial strip. Alternatively, the analysis of the strip approach can be assisted by specialists involved in farmers' OFE, such as researchers and consultants with appropriate knowledge and skills (Bramley et al, 2022;Lacoste et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Randomised designs have also been proposed (e.g. Bullock et al, 2019), although Bramley et al (2022) have argued that such designs may add unnecessary complexity to the trial process without providing additional value for guiding on-farm decisions. In both cases, treatments can be applied using variable rate technology (VRT), and data collection can be automated using yield monitors.…”

Section: Introductionmentioning

confidence: 99%

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A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making

2023

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The main difficulties grapegrowers and consultants face in obtaining robust trial results include time and labour to collect data and land variability that confounds trial results. Spatial approaches that use whole-field designs, sensing technologies and geostatistical analysis enable more efficient data collection and account for the impact of spatial variation on crop responses while generating statistically robust results. However, the practical application of these approaches for vineyard trials requires affordable automation of measurements of viticultural variables and access to skills for geostatistics. A strip approach has been developed to simplify experimentation by allowing the farmer to use a single crop row to trial and analyse data in a spreadsheet. However, guidance is needed as to how to position trial strips in a vineyard block to reveal likely treatment effects across the entire block. Here, we investigated using a covariate to a response variable of interest to position a strip trial to infer treatment effects beyond the trial strip. Strip trials were simulated for two experiments: one comparing three treatments for vineyard floor management on grape yield and another comparing two spray programs for powdery mildew control. Useful covariates for yield or mildew severity were determined using correlation analyses. Trial results were analysed using a moving pairwise comparison of treatments and a moving average of the covariates. Simulated trial strips that incorporated a range of variation in a useful covariate close to that encountered in the whole block showed how yield or mildew severity varied with the covariates along the strips. Importantly, such results provided information about likely crop responses in other parts of the block according to variation in the covariates, thus contributing to better-informed decision-making. Compared to whole-field approaches, this strip approach is more efficient and simpler for growers to implement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making

2023

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“…Our case studies below outline where collaborative efforts between growers and researchers led to better research outcomes (c.f. Bramley et al, 2022).…”

Section: Data Accessmentioning

confidence: 99%

Precision agricultural data and ecosystem services: Can we put the pieces together?

Robinson,

Schwinghamer,

Cárcamo

et al. 2023

Ecol Sol and Evidence

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Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions are expensive and time‐consuming to collect. Furthermore, relationships derived from small‐scale plot data may not apply to ecosystem services operating at larger spatial scales (fields and landscapes). Precision yield data (PYD) can be used to improve the accuracy and geographic range of ecosystem service studies but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis. We provide examples of analysis using simulated PYD, and outline two case studies of ecosystem services using PYD. Ecologists and agronomists should consider using PYD more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes.

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“…Spatially dense data from crop and soil sensors are now readily available and relatively inexpensive, enabling the characterization of the spatial variability of key factors that influence crop yields (Bramley et al, 2022). Several studies have examined the relationships between site-specific characteristics and yields to predict yields or develop MZs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability and temporal stability of corn response to nitrogen and seed rates in on-farm precision experiments

Alesso¹,

Martín²

2023

Preprint

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Geographically weighted regression (GWR) analysis and on-farm precision experiments (OFPE) allow the quantification of the within-field spatial variability of crop response to controllable inputs. Few studies have quantified the magnitude and consistency of these responses across years, and their relationship with site-specific characteristics. In this study we: (1) assessed the within-field spatial variability of corn response to nitrogen (NR) and seed (SR) rates using OFPE data and GWR models; (2) determined the spatial agreement between NR and SR response classes, and their temporal stability; and (3) modeled the effect of weather and site-specific features on the spatial distribution of the responses. Response maps were estimated by fitting GWR models to yields and as-applied NR and SR from 14 OFPE. Response maps classified into positive and non-positive response classes were spatially joined to weather, soil, and landscape covariates to train a random forest model. Zones with positive responses to NR and SR were detected in all fields. In most cases, these zones were inconsistent across years due to weather. The higher spatial agreement between NR and SR response classes for the same year suggests that factors controlling the crop response to these inputs are similar. In both cases, weather variables were the most important predictors followed by landscape and soil attributes. Whereas weather variables acted at a field level, i.e. field-year, the latter reflected the effect of within-field variability on crop response. To reduce this seasonal uncertainty, future research should consider conducting larger numbers of OFPE across multiple seasons.

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Did someone say “farmer-centric”? Digital tools for spatially distributed on-farm experimentation

Cited by 9 publications

References 66 publications

A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making

A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making

Precision agricultural data and ecosystem services: Can we put the pieces together?

Spatial variability and temporal stability of corn response to nitrogen and seed rates in on-farm precision experiments

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