Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a p...Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on clim展开更多
Determining biomass production of individual alfalfa (Medicago sativa L.) plants in space planted evaluation studies is generally not feasible. Clipping plants is time consuming, expensive, and often not possible if t...Determining biomass production of individual alfalfa (Medicago sativa L.) plants in space planted evaluation studies is generally not feasible. Clipping plants is time consuming, expensive, and often not possible if the plants are subjected to grazing. A regression function (B′ = 0.72558 + 0.11638 × V′) was developed from spaced plants growing on rangeland in northwestern South Dakota near Buffalo to nondestructively estimate individual plant biomass (B) from canopy volume (V). However, external validation is necessary to effectively apply the model to other environments. In the summer of 2015, new data to validate the model were collected from spaced plants near Brookings, South Dakota. Canopy volume and clipped plant biomass were obtained from ten alfalfa populations varying in genetic background, growth habit, and growth stage. Fitted models for the model-building and validation data sets had similar estimated regression coefficients and attributes. Mean squared prediction errors (MSPR) were similar to or smaller than error mean square (MSE) of the model-building regression model, indicating reasonable predictive ability. Validation results indicated that the model reliably estimated biomass of plants in another environment. However, the technique should not be utilized where individual plants are not easily distinguished, such as alfalfa monocultures. Estimating biomass from canopy volume values that are extrapolations (>2.077 × 10<sup>6</sup> cm<sup>3</sup>) of the model-building data set is not recommended.展开更多
文摘Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on clim
文摘Determining biomass production of individual alfalfa (Medicago sativa L.) plants in space planted evaluation studies is generally not feasible. Clipping plants is time consuming, expensive, and often not possible if the plants are subjected to grazing. A regression function (B′ = 0.72558 + 0.11638 × V′) was developed from spaced plants growing on rangeland in northwestern South Dakota near Buffalo to nondestructively estimate individual plant biomass (B) from canopy volume (V). However, external validation is necessary to effectively apply the model to other environments. In the summer of 2015, new data to validate the model were collected from spaced plants near Brookings, South Dakota. Canopy volume and clipped plant biomass were obtained from ten alfalfa populations varying in genetic background, growth habit, and growth stage. Fitted models for the model-building and validation data sets had similar estimated regression coefficients and attributes. Mean squared prediction errors (MSPR) were similar to or smaller than error mean square (MSE) of the model-building regression model, indicating reasonable predictive ability. Validation results indicated that the model reliably estimated biomass of plants in another environment. However, the technique should not be utilized where individual plants are not easily distinguished, such as alfalfa monocultures. Estimating biomass from canopy volume values that are extrapolations (>2.077 × 10<sup>6</sup> cm<sup>3</sup>) of the model-building data set is not recommended.
