Soil pH and Lime Management for Corn and Soybean

The objectives of this work were to (1) asses how Shoemaker-McLean-Pratt (SMP), Sikora, and Mehlich buffers, titratable acidity, and soil properties relate to soil pH change due to liming, (2) study the variation of soil pH and crop response to lime within fields, (2) identify optimum soil pH for corn and soybean, (3) evaluate the effect of subsoil pH on crop response to lime in Iowa, and (4) evaluate the rate of soil pH increase from application of different lime sources. Fourteen replicated strip trials with lime were established in Iowa from 2007 to 2009 using precision agriculture technologies and were evaluated two, three, or four years. Soil samples were collected before applying lime and also after each crop harvest. Also, four replicated field small plot trials with rates and sources of lime were established in 2009. Soil samples were collected at eight sampling dates during a period of 16 months following liming. Within-field initial soil pH variation varied widely across fields. Sikora and SMP were highly correlated, did not differ for most soil series. Soil pH was by far the best variable to predict pH change variation. Limestone application significantly increased soil pH in all sites, and maximum pH values were generally reached during the second year after liming. Crop grain yield was increased due to liming in 12 of 42 site-years. The yield response to lime did not differ between crops. Crop response decreased with increasing initial soil pH, but was highly affected by subsoil pH. Optimum pH range for corn and soybean was 6.0-6.5 for soil series with subsoil having pH\u3c7.0 but was significantly lower (pH 5.0-5.5) for soils with subsoil having a higher pH. Results from the small plots showed that the soil pH increase over time for the three lime sources was curvilinear with decreasing increments to a plateau maximum that was reached about 100 days after liming. However, the early pH increases and maximum pH reached were greater for pure CaCO3 than for the calcitic and dolomitic limestones. Results showed that the time of limestone reaction in the soil was faster than usually assumed

Soil pH Change over Time as Affected by the Limestone Sources and Application Rate

Limestone is commonly used in Iowa to maintain optimum soil pH for crop production. There is insufficient information, however, concerning the reaction time in the soil and short-term effects on crop yield for different sources and application rates. This information is needed to improve soil pH management and lime recommendations. The objective of this study was to study the soil pH and crop response to the application of pure calcium carbonate (CaCO3), and calcitic or dolomitic limestone in a typical acid soil of central Iowa

Change of Soil pH over Time as Affected by Lime Sources and Application Rates

Agricultural limestone is commonly used in Iowa to maintain optimum soil pH for crops. However, there is insufficient information concerning the reaction time of lime sources in the soil and short-term effects on crop yield for different sources and application rates. This information is needed to improve soil pH management and lime recommendations. The objective of this study was to study the soil pH and crop response to the application of pure calcium carbonate (CaCO3), and calcitic or dolomitic limestone in a typical acid soil of northeast Iowa

Soil pH Change as Affected by the Lime Source and Application Rates

Agricultural limestone is commonly used in Iowa to maintain optimum soil pH for crops. There is insufficient information, however, concerning the reaction time of lime sources in the soil and short-term effects on crop yield for different sources and application rates. This information is needed to improve soil pH management and lime recommendations. The objective of this study was to study the soil pH and crop response to the application of pure calcium carbonate (CaCO3), and calcitic or dolomitic limestones in a typical acidic soil of southwest Iowa

Soil pH Change over Time as Affected by Sources and Application Rates of Liming Materials

Agricultural limestone is commonly applied to maintain optimum soil pH for crops. There is insufficient information, however, about short-term effects of different lime sources and application rates on soil pH changes over time and crop yield. This information is needed to improve soil pH and lime management. The objective of this experiment was to study the soil pH and crop yield response to application of pure calcium carbonate, calcitic limestone, and dolomitic limestone

Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used and yields obtained by maize producers in that region. Dynamic factors alone explained 50% of the variability in the EONR whereas static factors explained only 20%. Best EONR predictions resulted by combining one static variable (soil depth) together with four dynamic variables (number of days with precipitation\u3e20 mm, residue amount, soil nitrate at planting, and heat stress around silking). The resulting EONR model had a mean absolute error of 39 kg N ha−1 and an adjusted R2 of 0.61. Interestingly, the yield of the previous crop was not an important factor explaining EONR variability. Regression models for yield at optimum and at zero N fertilization rate as well as regression models to be used as forecasting tools at maize planting time were developed and discussed. The proposed regression models are driven by few easy to measure variables filling the gap between simple (minimum to no inputs) and complex EONR prediction tools such as simulation models. In view of increasing data availability, our proposed models can be further improved and deployed across environments. Includes supplemental figures and table. Excel model attached below as additional file

Corn and soybean response to soil pH level and liming

Limestone application to raise soil pH is needed when the pH is too acidic to allow for optimum crop growth and yield. Some Iowa soils are naturally acidic, and others become acidic over time mainly due to sustained N application for corn with urea or ammonium-based products that acidify soils during the microbial transformation of ammonium to nitrate (nitrification). Soil pH decreases as the acidity increases because the pH measurement expresses acidity as the negative logarithm of H+ ion concentration. Alfalfa is the most sensitive crop to low pH grown in Iowa, while forage grasses are the least sensitive and corn and soybean are intermediate. Soil acidity can affect plant growth directly or indirectly by affecting the plant-availability of several nutrients, increasing levels of some elements to phytotoxic concentrations, and influencing microbial activity or other soil properties

Soil pH and Lime Management for Corn and Soybean

The objectives of this work were to (1) asses how Shoemaker-McLean-Pratt (SMP), Sikora, and Mehlich buffers, titratable acidity, and soil properties relate to soil pH change due to liming, (2) study the variation of soil pH and crop response to lime within fields, (2) identify optimum soil pH for corn and soybean, (3) evaluate the effect of subsoil pH on crop response to lime in Iowa, and (4) evaluate the rate of soil pH increase from application of different lime sources. Fourteen replicated strip trials with lime were established in Iowa from 2007 to 2009 using precision agriculture technologies and were evaluated two, three, or four years. Soil samples were collected before applying lime and also after each crop harvest. Also, four replicated field small plot trials with rates and sources of lime were established in 2009. Soil samples were collected at eight sampling dates during a period of 16 months following liming. Within-field initial soil pH variation varied widely across fields. Sikora and SMP were highly correlated, did not differ for most soil series. Soil pH was by far the best variable to predict pH change variation. Limestone application significantly increased soil pH in all sites, and maximum pH values were generally reached during the second year after liming. Crop grain yield was increased due to liming in 12 of 42 site-years. The yield response to lime did not differ between crops. Crop response decreased with increasing initial soil pH, but was highly affected by subsoil pH. Optimum pH range for corn and soybean was 6.0-6.5 for soil series with subsoil having pH<7.0 but was significantly lower (pH 5.0-5.5) for soils with subsoil having a higher pH. Results from the small plots showed that the soil pH increase over time for the three lime sources was curvilinear with decreasing increments to a plateau maximum that was reached about 100 days after liming. However, the early pH increases and maximum pH reached were greater for pure CaCO3 than for the calcitic and dolomitic limestones. Results showed that the time of limestone reaction in the soil was faster than usually assumed.</p

Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used and yields obtained by maize producers in that region. Dynamic factors alone explained 50% of the variability in the EONR whereas static factors explained only 20%. Best EONR predictions resulted by combining one static variable (soil depth) together with four dynamic variables (number of days with precipitation\u3e20 mm, residue amount, soil nitrate at planting, and heat stress around silking). The resulting EONR model had a mean absolute error of 39 kg N ha−1 and an adjusted R2 of 0.61. Interestingly, the yield of the previous crop was not an important factor explaining EONR variability. Regression models for yield at optimum and at zero N fertilization rate as well as regression models to be used as forecasting tools at maize planting time were developed and discussed. The proposed regression models are driven by few easy to measure variables filling the gap between simple (minimum to no inputs) and complex EONR prediction tools such as simulation models. In view of increasing data availability, our proposed models can be further improved and deployed across environments. Includes supplemental figures and table. Excel model attached below as additional file