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In light of the rapidly growing agricultural sector, spotting signs of climate change is crucial. Even a slight change in climate behaviour can hinder crop development. As a result, it is necessary to investigate these climatic signals at a localised regional scale. The current methods mainly emphasise on outliers when identifying abnormalities in geographical and temporal data. This disregards the possibility of negative impacts brought on by climatic changes at each location. This paper introduces a novel mathematical approach, named the probabilistic convolution neighborhood technique (PCNT), that finds the temporal and spatial coherence at each point. Its ability to examine distribution at all grid points makes it easy to identify even a slight change in climatic variables. This new approach has been applied to study the variations in parameters, namely humidity, precipitation, mean, and maximum temperatures, that are collected from the Indian Monsoon Data Assimilation and Analysis (IMDAA) for the time period 1980 to 2020. The datasets are daily single-level datasets from which annual and seasonal (Kharif, Rabi, and Zaid) data are obtained. Spatio-temporal properties, along with inter-vicennial and inter-decadal variations of climatic variables with regions having low, high, and normal probability distributions, are studied. The findings showed a pattern in the temporal features between Kharif and Rabi. The mean temperature for the annual and seasonal data showed decreasing trends for tail diagnostics. The study brings out an understanding in the probable change in various metrics, which are crucial in the context of crop growth and agricultural practice. [ABSTRACT FROM AUTHOR]

The existing Laspeyres index, Paasche's index, and Fisher's index under classical statistics cannot be applied when observations are indeterminate, imprecise, and uncertain. In this paper, the Laspeyres index, Paasche's index, and Fisher's index are introduced to deal with the data having neutrosophy. We present these indexes when indeterminacy is present in the data. To see the aggregate change in the production of rice crops due to climate change under an uncertain environment, data is collected from nine districts of Punjab, Pakistan. From the data and comparative studies, it is concluded that the proposed indexes are more informative, flexible, and suitable to be applied under indeterminate conditions. [ABSTRACT FROM AUTHOR]

Precipitation variability imposes significant pressure in areas where agricultural practice is dominated by smallholder farmers who are dependent on subsistence farming. Advances in the understanding of this variability, in both time and space, have an important role to play in increasing the resilience of agricultural systems. The need is particularly pressing in regions of the world such as the African continent, which is already affected by multiple stresses including poverty and economic and political instability. In this paper, we explore the use of generalised linear models (GLMs) for this purpose, via a case study from north-east South Africa. A GLM is used to link the local rainfall variability to large-scale climate drivers identified from previous subcontinental-scale analyses, and the ability of the resulting model to simulate precipitation features that are relevant in agricultural applications is evaluated. We focus in particular on a set of growing season indices, proposed for the investigation of intraseasonal characteristics relevant for maize production in the region. Seven indices were computed from spatially averaged daily rainfall series from nine stations in the study area. As a first attempt to use GLMs for this type of application, the results are encouraging and suggest that the models are able to reproduce a range of agriculture-relevant indices. However, further research into spatial correlation structure is recommended to improve the multisite generation of the rainfall-derived characteristics. [ABSTRACT FROM AUTHOR]