Bahiagrass (Paspalum notatum Flüggé) has poor production, if established on granitic regosol throughout Japan. Effects of cattle manure (50 t/ha) and chemical fertilizer (50 kg/ha, each of nitrogen, phosphoric acid and potassium oxide) application on bahiagrass dry matter production grown on a granitic regosol were investigated in a pot (16.0 cm diameter×19.0 cm depth, 200 cm² area) experiment. Bahiagrass grew in the limited extent without manure or chemical fertilizer, and manure application greatly improved dry matter production, whereas chemical fertilizer application had only a limited effect due to one tenth of nitrogen application compared with manure. Treatments with sole of manure and combination of both manure and chemical fertilizer achieved the similar dry matter production. Manure application positively affected concentrations of available phosphoric acid, exchangeable potassium and total nitrogen in the pot soil, while chemical fertilizer under the present application level did not have such effects. Improvement of soil chemical composition with manure application should be linked with enhancement of bahiagrass production on a granitic regosol.

The prediction of maize harvesting time is needed to conduct harvesting operation effectively. Maize maturity is related to grain moisture content which is affected by weather condition and cultivars. The objective of this study is to estimate the effects of weather condition and cultivars on maize grain moisture content for predicting harvesting time. We investigated grain moisture content several times during the ripening period using a total of 20 cultivars since 2015 to 2018 at Tohoku Agricultural Research Center in Morioka. We analyzed the relationships between grain moisture content, cultivars, and the accumulation values of temperature, relative humidity, and wind velocity. Further, we made linear regression models to estimate grain moisture content using the data of 2015 and 2016 and estimated the accuracy using the data of 2017 and 2018. The result shows that the accumulation values of temperature, relative humidity, and wind velocity significantly affected grain moisture content. The main effect of cultivars improved the estimation accuracy, but interactions of cultivars and accumulation values of weather data deteriorated. This study achieved a high estimation for grain moisture content with a linear regression model that contained the main effects of cultivars and accumulation values of temperature, relative humidity, and wind velocity.

In 2016 the American Statistical Association (ASA) issued a recommendation on p-values. I discuss several problems in the p-values and the statistical tests employed in grassland/agricultural sciences. The ASA points out that even if the null hypothesis is accepted, this does not imply that the means are equal. Although the p-value is informative, business policies or scientific/technical decisions should not be based only on p-values and statistical tests. It is essential to consider, among other types of available information, any scientific data obtained in experiments/surveys, social requirements and past experiences. I agree with these ASA opinions. I have roughly explained the differing concepts for the p-value and statistical tests between the Fisher and Neyman-Pearson schools. The statistical tests used by the Neyman-Pearson school are appropriate for the development of new technologies, while the statistical tests and p-value interpretations used in the Fisher school are appropriate for confirming new scientific knowledge such as inventions and discovery. Some multiple pairwise comparisons like the t-test influence p-values, which must be taken into consideration in policy decisions. The fundamental underlying theories of statistical tests, estimation, and practice/training should be essential elements in statistical education.