The Power of Lime: Achieving Higher Yields in row crops with Lime Usage in the First Year
Updated: Mar 15
Results of mYard Digital Field Trials of calcium oxide on spring crops showed that while a significant increase in yield was seen in the sunflower, no statistical significance was seen on corn. Indicating that the optimal use of lime depends on the crop, soil type, and environmental conditions. For oil crops which has high Ca need the application of accessible CaCO3 content products will increase your yields beyond the support on soil recovery.
As an agronomist, we understand the importance of calcium oxide (CaCO3), otherwise known as lime, in agricultural practices - this is part of our 3rd pillar in our mYard vision. Lime is essential for pH recovery in soil and is used to increase the availability of soil nutrients such as phosphorus and nitrogen. This, in turn, increases the efficiency of crop production. Lime also reduces soil acidity, which can inhibit the uptake of nutrients by the plant roots, leading to poor growth and low crop yields. Additionally, lime improves soil physical properties such as aeration, water holding capacity, and drainage, which are all important for healthy crop growth.
Lime is an essential element of a longterm crop production strategy.
Measuring the effect of lime in row crops is an essential step determining the potential for yield increase. By testing various levels of lime, we can ascertain the best amount for optimal growth, which can then be used to define variable rate maps to increase crop yields. The chance of variation in nutritional elements in larger agronomic fields is dependent on a variety of factors. These factors include soil type, soil fertility, crop management practices, climate, and terrain.
The best model to determine the yield impact of lime usage would be a linear regression model. As our trial design is already applied variable application as a first step we did a One Way ANOVA* analysis to confirm if lime usage - determined by the field needs lime need - has positive yield impact on the first year?
We ran one CORN and one SUNFLOWER trial Field. These are single field trials analysed by the mYard Digital Field Trialing methodology** which provides high statistical power based on the increased number of samples zebra stripe design of application map and the solid data cleaning process.
SUNFLOWER Trial Results
Field CaCO3 level mean is 3.9% with a range of [1.9-4.9]
Crop CaCO3 needs: ~90kg/t for the crop 35kg/t removed by grain
In this trial the crop has high CaCO3 need and the Field had low supply of lime. Our expectation that the field trail will show significant difference, which was visually visible after our analysis:
The results confirmed high statistical significance (99.95% probability that the treated/non treated area yield is different), and an estimated +231kg Yield increase on the treated paths.
Image: the increased yield results pattern is almost identical with the treatment path
The samples were grouped by range and the analysis compared sample results that were spatially close to each other. This allowed for the generation of average treated/non-treated values for the ANOVA analysis. A total of 8392 treatment-yield pairs were patterned to 104 treated/non-treated samples. Only pairs in which both groups were represented were kept.
After we ran the ANOVA analysis our visual impression is confirmed by statistics.
Source of Variation
ChatGPT translation for teh ANOVA tabel above:
The Between Groups row shows that there is a significant difference between the two groups, as indicated by the F-value of 12.48240062, which is much larger than the F critical value of 3.88699627. The P-value of 0.000507038 also indicates a significant difference between the two groups. The Within Groups row shows that there is no significant difference within the groups, as indicated by the MS value of 0.22376265. The Total row shows the sum of the SS and df for both the Between Groups and Within Groups rows.
We have evidence that our treatment has a positive effect on yield. To understand the magnitude of this impact, we can run a linear regression to determine the increase in kg/ha yield for every 1 kg/ha of lime application.
The intercept value of 4083.49kg indicates that the averaged Field potential was at 4083.49kg (4t-ha) when the treatment rate is 0. The treatment rate value of 2.41kg indicates that for every kg increase of lime applied, the Sunflower yield is expected to increase by 2.41kg. The t-stat and p-value values indicate that the treatment rate is statistically significant. In other words, the treatment rate has a significant effect on the outcome. We have to acknowledge that in the study we differentiated lime application based on soil sampling and applied it at different rate based on the soil CaCO3 availability which should increase the effectiveness of the treatment.
Although the impact of lime application on yield increase is significant, the regression R2 is low (0.2), indicating that lime is a minor factor in the final yield achievement. The standard error of the regression is high (830kg), meaning the measured values have a high variation against the prediction. This would be unmeasurable without using digital technologies and statistics on thousands of data points.
CORN Trial Results
Field CaCO3 level mean is 5.2% with a range of [3.1-8.5]
Crop CaCO3 needs: ~3kg/t for the crop 1kg/t removed by grain
In this trial, the crop had low CaCO3 needs and the field had an optimal supply of lime. Therefore, the results should qualify whether lime could support increased nutrition access and water-holding capacity during the application season. As the field's pH was 7.2, the impact of nutrition access should be low. However, since the average harvested corn yield of the field was 2.8t/ha, external factors such as available water, organic matter percentage, and the impact of erosion become increasingly important. We will further analyze these factors in our next article.
The results has NO statistical significance (67% probability that the treated/non treated area yield is different), and an estimated +52kg Yield increase on the treated paths.
Source of Variation
ChatGPT translation for teh ANOVA tabel above:
The sum of squares (SS) for the difference between the groups was 0.198329631, which was then broken down into the mean square (MS) by dividing it by the degrees of freedom (df) of 1. This MS value was then used to calculate the F-statistic (F) which was found to be 0.615621576. This F-statistic was then compared to the critical F value (F crit) of 3.873724232, and the associated p-value (P-value) of 0.433319458 was found to be larger than the alpha level of 0.05, which indicates that there is no significant difference between the groups. The SS, df, and MS for the within-groups variation was also indicated.
Corn we see minor impact therefore we did not shared the Linear Regression in details even if it showed significant p value, we don't feel it representative.
Sunflower we see high impact and very strong x2.4 Yield return on the lime application. The low R2 value and large standard error in the regression suggest that other factors have a greater influence on yield then lime application. While the importance of macro-nutrients is well known, this study attempted to demonstrate the short-term benefit of lime application to help farmers calculate the financial impact of a nutrition which is mainly considered for soil conservation, and balancing acid soil pH. Hopefully, these trials will draw attention to the value of lime application in row crops and make it a standard element of crop rotation plans, particularly before oil crops.
I'd like to thank my brother, Marton Fehervari, for helping this program execute on the Fields and managing all farm operations. I'm also grateful to my wife and son for allowing me to disappear for a several hours while this project reached completion.
One Way ANOVA (Analysis of Variance) is a statistical technique used to compare the means of two or more groups. It is used to determine whether there is a statistically significant difference between the means of the groups. The model is used in situations where there are multiple samples and the researcher wants to compare the means of the samples. In this model, the dependent variable is the mean of the samples and the independent variables are the factors that may have an effect on the dependent variable. ANOVA can be used to compare the means of several groups, determine if there is a statistically significant difference between the means, and identify which factors may have had an effect on the means.
This multi-step method assesses a single agronomic factor. First, it cleans all YieldMaps from outliers of speed and yield, plus interpolates them to yield results and minimize the statistical influence of low-quality data. A similar process is applied to application maps. Then, all inputs are mapped with a treatment path aligned grid defining the sampling points for analysis. This method helps to generate a high number of comparable data points for a statistically sound agronomic decision.
mYard Field Trialing method demonstration
Image: Grided Yield samples with aligned Spraying Treatment paths.