Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

Griffin, Terry; Dobbins, Craig L.; Vyn, Tony J.; Florax, Raymond J.G.M.; Lowenberg‐DeBoer, James

doi:10.1007/s11119-008-9072-2

Cited by 44 publications

(27 citation statements)

References 4 publications

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“…The yield data files firom the farmers' spatially referenced yield monitor were processed using SMS Advanced version 11.5 (Ag Leader Technology,Ames, Iowa). In order to improve data quality, the processed data were exported to Yield Editor, version 2.0.2 (USDA ARS 2012), for the removal of erroneous yield data points (Drummond 2011).The importance of improving the quality level of the yield monitor data on making inferences has been stated by Griffin et al (2008). The filtering criteria used were start pass delay, end pass delay, minimum swath width, and minimum and maximum yield.…”

Section: Tablementioning

confidence: 99%

Crop yield evaluation under controlled drainage in Ohio, United States

Ghane¹,

Fausey²,

Shedekar³

et al. 2012

Journal of Soil and Water Conservation

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Controlled drainage (CD) is an important practice for reducing nutrient loading to surface water bodies across the midwestern United States. There may also be a positive crop yield benefit, which could add an incentive for adoption of this practice.The objective of this multienvironment trial was to assess yield stability and yield performance of CD in northwest Ohio, United States. The trial was a split-plot experiment with environments as whole plots (randomization unit). The main plot factor was crop w^ith three levels: corn {Zea mays L.), popcorn (Zea mays L. var. everta), and soybean (Glycine max [L.] Merr.).The subplot factor was drainage management with two levels: conventional fi-ee drainage (FD) and CD. The design of the main plot factor was a completely randomized design. Mixed model analysis showed that CD management produced a statistically greater (p-value = 0.0246) crop yield compared to FD management over 23 site-year environments during 2008 to 2011. Interaction between drainage management and crop was not significant, implying that CD management had the same yield-increasing effect for all crops.The CD management provided 3.3%, 3.1%, and 2.1% greater yield for corn, popcorn and soybean, respectively, relative to the FD management.The stability analysis based on 23 environments suggested that the drainage managements were not different in yield stability, though a larger number of environments are needed to make a more accurate assessment of yield stability. Area of influence analysis indicated that CD can provide more profit than FD for relatively flat fields where the influence of CD extends over the entire field. In conclusion, CD provided crop yield advantage over FD across different environments in northwest Ohio.

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Section: Tablementioning

confidence: 99%

Crop yield evaluation under controlled drainage in Ohio, United States

Ghane¹,

Fausey²,

Shedekar³

et al. 2012

Journal of Soil and Water Conservation

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“…Currently, precision agriculture technologies can help identify potential site-specific factors that influence economic YR to Instinct. Farmers can use yield monitoring technologies and GPS to evaluate performance of many fertilizer products and technologies on their fields across a wide range of management practices, soil types, and weather conditions (Blackmer and Kyveryga 2010;Griffin et al 2008). …”

Section: Introductionmentioning

confidence: 99%

Probability of profitable yield response to nitrification inhibitor used with liquid swine manure on corn

Kyveryga

Blackmer

2013

Precision Agric

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Nitrification inhibitors (NI) can be used with liquid swine manure (LSM) to decrease potential NO 3 losses, but knowledge specifying when and where NI can increase corn (Zea mays L.) yields is limited. Eleven on-farm evaluation trials (OET) were conducted in 2009 and 15 in 2010 to identify site-specific factors for using Instinct (an encapsulated form of nitrapyrin) with LSM in Iowa. Farmers injected LSM in the fall in at least three field-long strips with and without NI. Yield responses (YR) to NI were calculated by dividing yield monitor data into 50-m cells within each field. Hierarchical models were used to estimate predictive probabilities of profitable YR for two categories of monthly average rainfall and soil drainage. ) than fields receiving\90 cm rainfall. Within-field variability in YR was about four times greater than among-field variability, but within field-level factors had no significant effects on YR. The NI effects may not have lasted long enough to increase yields across all OET and predictive probabilities suggest that NI may produce profitable YR only when spring and summer rainfall exceed the long-term averages by more than 40 %.

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“…They own the data, but summaries of individual evaluations not identifiable by farmer are publicly available (ISA 2010). The analysis of data and the decision support system for adaptive management evaluations also differ from that of controlled small-plot experiments as adaptive management studies offer more practical and management-oriented decision support systems that can be used for better predictions of management outcomes under uncertainty (Neyberg et al 2006).While yield monitoring and GPS technology can be effectively used in on-farm evaluations, a large percentage of farmers across the Midwest use yield monitoring technology without analyzing the observed data (Griffin et al 2008). To engage farmers in on-farm evaluations, a special program was developed to collect feedback information about the N status from many cornfields across Iowa (Kyveryga et al 2010).…”

mentioning

confidence: 99%

“…While yield monitoring and GPS technology can be effectively used in on-farm evaluations, a large percentage of farmers across the Midwest use yield monitoring technology without analyzing the observed data (Griffin et al 2008). To engage farmers in on-farm evaluations, a special program was developed to collect feedback information about the N status from many cornfields across Iowa (Kyveryga et al 2010).…”

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Late-season digital aerial imagery and stalk nitrate testing to estimate the percentage of areas with different nitrogen status within fields

Kyveryga¹,

Blackmer²,

Pearson³

et al. 2011

Journal of Soil and Water Conservation

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Farmers are rapidly adopting various precision agriculture technologies (PAT), such as yield monitoring, variable-rate fertilizer applications, and remote sensing tools (Griffin et al. 2008). Spatial and temporal variability in soil properties, plant characteristics, and crop yields can be used Abstract: Precision agriculture technologies offer potential economic and environmental benefits from site-specific management of nitrogen (N) fertilizer and animal manure sources for corn (Zea mays L.). However, lack of knowledge and reliable methodology for developing and evaluating site-specific N fertilizer recommendations are the major obstacles for realizing these potential benefits. The objective of this study was to evaluate corn N status at the field scale and across many fields using late-season digital aerial imagery and the end-of-season corn stalk nitrate test in large-scale on-farm evaluation studies. About 30 groups of farmers, lead by agronomists and crop consultants, were formed across Iowa to evaluate different N management practices. Late-season color digital aerial imagery and digital soil maps were used to guide the collection of the corn stalk nitrate test samples within 683 cornfields in 2006, 824 fields in 2007, and 828 fields in 2008. Four areas-one from each of the three predominant soil types and one within a target-deficient area-were sampled in each field. Multilevel binary logistic regressions were used to quantify the relationship between green reflectance of the corn canopy and corn N status, expressed as deficient and sufficient (a combination of marginal, optimal, and excessive categories of the corn stalk nitrate test), within and across the fields. Percentages of areas within fields with deficient and sufficient N status were estimated using distributions of pixel counts of green reflectance of the corn canopy. Multiple regression analysis was used to identify factors affecting percentage-deficient area within the fields. Results showed that N management category (a combination of N form and timing of application) and early season rainfall (May, June, or cumulative from March through June) had the largest effects on percentage-deficient area. Fields with liquid swine manure applied in the fall or urea-ammonium nitrate solution applied in the spring (before planting) or urea-ammonium nitrate solution applied at sidedress had larger areas of N deficiency than fields with anhydrous ammonia applied in the spring. Larger early season rainfalls also increased percentage-deficient area during each year. The results of large-scale evaluations can be used to develop more accurate site-specific N recommendations based on knowledge of differences between management practices and effects of soil properties and rainfall on N status within fields. Future evaluations can identify areas that persistently have excessive N status and quantify potential N fertilizer reductions within those areas or fields.Key words: adaptive management-corn stalk nitrate test-digital aerial imagery-nitrogen management-pre...

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Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

Cited by 44 publications

References 4 publications

Crop yield evaluation under controlled drainage in Ohio, United States

Crop yield evaluation under controlled drainage in Ohio, United States

Probability of profitable yield response to nitrification inhibitor used with liquid swine manure on corn

Late-season digital aerial imagery and stalk nitrate testing to estimate the percentage of areas with different nitrogen status within fields

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