Preface

**Chapter 8 113**

**Chapter 9 129**

**Chapter 10 145**

**Chapter 11 161**

**Chapter 12 171**

**Chapter 13 193**

The Role of Biosensor in Climate Smart Organic Agriculture toward Agricultural and Environmental Sustainability

Efficacy of Risk Reducing Diversification Portfolio Strategies among Agro-Pastoralists in Semi-Arid Area: A Modern Portfolio

Arabian Sea Tropical Cyclones: A Spatio-Temporal Analysis in Support of Natural Hazard Risk Appraisal in Oman *by Suad Al-Manji, Gordon Mitchell and Amna Al Ruheili*

Effects of Demographic Characteristics for Farmers' to Climate

Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study of *Bromus inermis* Pasture

*by Taity Changa, Jane Asiyo Okalebo and Shaokun Wang*

Beetles and Meteorological Conditions: A Case Study

*by Salisu Lawal Halliru, Abubakar Abdullahi Bichi* 

*by Kingsley Eghonghon Ukhurebor*

Change in Bunkure, Nigeria

in Eastern Nebraska

**II**

*and Aliyu Shu'aibu Muhammad*

*by Marcos Paulo Gomes Gonçalves*

Theory Approach *by Ponsian T. Sewando* Agrometeorology involves climatology, meteorology, agrology, biology, and hydrology. It requires a multidisciplinary range of data for operational applications and research. Current concerns for the sustainability of agroecosystems in different sectors of agriculture have heightened awareness for the careful use of natural resources. For proper and efficient use of soils and plant/animal genetic material, knowledge of the role of climate is essential. A more inclusive definition of agricultural meteorology deals with water, heat, air, and related biomass development, above and below ground, in the agricultural production environment, including the impact of pests and diseases that also depend on these factors. Using remote sensing (RS), Global Positioning Systems (GPS), and Geographic Information Systems (GIS) to develop crop inventories helps decision-makers and planners with best practices for agricultural management. The data analysis of crop details using various geospatial technologies fills in gaps in statistical agriculture research and provides reliable, replicable, and efficient methods for generating agricultural statistics.

This book includes a series of pictures that illustrate the agrometeorological features fundamental to the understanding of the subject. It contains thirteen chapters organized in order of increasing complexity. The chapters discuss the definitions, aims, scopes, and importance of agricultural meteorology and various meteorological parameters such as radiation, air temperature, air pressure, winds, humidity, and evaporation/evapotranspiration.

This volume is designed for students and researchers who desire to further devote their careers to the protection of natural ecosystems and increasing agricultural productivity and sustainability.

> **Ram Swaroop Meena**  Department of Agronomy, Institute of Agricultural Sciences (BHU), Varanasi, UP, India

**1**

**Chapter 1**

**Abstract**

**1. Introduction**

increasing climate variability.

Changing Climate

externalities that can contribute to yield change.

*Godfrey Shem Juma and Festus Kelonye Beru*

Prediction of Crop Yields under a

The impact of increasing climate variability on crop yield is now evident. Predicting the potential effects of climate change on crops prompts the use of statistical models to measure how the crop responds to climate variables. This chapter examines the usage of regression analysis in predicting crop yield under a changing climate. Data quality control is explained and application of descriptive statistics, correlation analysis and contingency tables discussed. Methodological aspects of crop yield modeling and prediction using climate variables are described.

Estimation of yield via a multilinear regression approach is outlined and an overview of statistical model verification introduced. The study recommends the usage of regression models in estimating crop yield in consideration of many other

In this chapter, we describe an experimental approach that can be employed in predicting crop yield in a changing climate. An introductory applied approach to

Climate change is now evident with well documented socio-economic impacts that will affect food production [1, 2]. The decline in food production corresponding to reduction in crop yields can be investigated using statistical models [3, 4]. While climate related factors can affect yield of crop, there are other externalities that can impact on yield production that include the quality of soil, usage of commercial fertilizers or organic manures and residual effects of chemical substances in soils [5, 6]. **Figure 1** below shows the projected changes in crop yield due to the impacts of

Increasing climate variability and associated uncertainties, its impacts on food production and general livelihoods prompts the usage of prediction models to estimate future food production for early warning and planning. Projection of crop yield in a changing climate has been identified with uncertainties that are continuously being reduced by improvement in climate parameter response functions, including temperature [7]. Developing countries have also been identified with weaker monitoring and reporting of crop health which can lead to absence of early warning systems and slow responses to droughts and potential food shortages [8]. The prediction models employed are broadly classified into statistical or dynamic (mechanistic). However, the modeling has been in some instances enhanced by artificial neural network technology that has been applied to generate regional

**Keywords:** crop, yield, prediction, climate change, regression model

linear statistical modeling and correlation analysis is examined.
