Explore the vast range of titles available.

Indian Physicist Dr C.V. Raman, the founder of the Raman Spectroscopy, is the only Indian who received Nobel Prize in Science. Raman trained almost 100 scientists in his laboratory who influenced the development of science and technology in India. Dr L A Ramdas was one of them who began his research career under Raman in the beginning of 1920s. Not only, he coined the term ‘Raman Effect’, but also studied the scattering of light in gases and vapours. The present book written by Dr Rajinder Singh, presents Ramdas’s work on light scattering in association with Raman, his venture in establishing a new field namely, Agricultural Meteorology, and subsequently the discovery of Ramdas Layer, named after him.

Lately, an increase in the commercialization of gladiolus has been observed, making it necessary to know information that contributes to the optimization of its production. Such information can be obtained from the scientometric analysis. Thus, the objective of this work was to perform a scientometric analysis of the global scientific literature to quantify the studies on gladiolus and to specify in the Brazilian scientific literature the resultsof works on the cultivation environment. The scientometric analysis was performed in the Scopus database for the entire historical data series until the year 2021. A total of 1402 scientific papers published on gladiolus culture were obtained with an average publication rate equal to 0.62 papers year-1. These papers were mostlypublished as scientific articles in English language in journals focusing on horticulture and India is the countrywith the highest number of publications. About the cultivation environment, the studies conducted in Brazil indicate that the production of gladiolus should be carried out in periods that do not occur frosts and also for average air temperature below 35°C and soil humidity above 75% of field capacity. Despite the important results of these works, the global scientific literature still lacks more information that adequately assists in the increase of gladiolus production.

The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.

Crop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase‐wise crop advisory, and advisory for harvest were prepared using long‐term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r2 of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022–23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day −1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R 2  = 0.71, mean biased error = 0.04 mm day −1 , root mean squared error = 0.62 mm day -1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model’s capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

The identification of the spatial and temporal variability of meteorological variables, as well as of climate extreme events, such as the duration of dry spells, duration of warm spells and rainfall intensity, is crucial for agrometeorological studies, since they can negatively impact yields through the exposure of the crop to critical conditions. Thus, this study analyzed trends in 23 rainfall and temperature climate extreme indices in the MATOPIBA region, currently a strategic area for investments in soybean cultivation, playing a major role in ensuring global food security. Daily time series of rainfall and temperature (maximum and minimum) data in the 1980–2013 period were used, arranged in a 0.25° × 0.25° grid, covering 963 points over the studied region. The data set was submitted to cluster analysis, the Mann–Kendall non-parametric test and extremes indices and their trends were estimated through the RClimdex software. Trends in rainfall rates and in mean, maximum and minimum temperatures were evaluated for each cluster and shifts from the climatological patterns of these variables were detected. Only some of the rainfall climate extreme indices presented significant increase and/or decrease in the CI and CII subregions. On the other hand, there was a significant increase in almost all temperature climate extreme indices in all clusters. The persistency of these trends may lead to impacts on soybean cultivation in the MATOPIBA region, and therefore these results are crucial for the elaboration of strategies for agricultural planning.

This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.

Methodological criteria for data quality control with geophysical range and spectrum consistency were evaluated, establishing flags and quality indicators for soil moisture data records, in a range of depths between 10, 30, and 50 cm, from automatic agro-meteorological stations located in the most important agricultural regions of Colombia. Data for analysis were collected from 105 stations of the IDEAM network, in an observation window from 2001-2020. The results showed that 40.3% of the soil moisture data were of good quality, 12.9% were questionable due to spectrum flags, 14.3% were questionable due to geophysical range and 32% were erroneous because the values were not possible and/or missing. The depth closest to the surface had the highest number of quality flags, suggesting that the soil layer has the highest error detection rate associated with soil moisture condition recording; the most common quality flag was C02: “Soil moisture >60% & ≤100%”, detected in 93% of the sensors, and the second most frequent flag was C01: “Soil moisture ≥0% & <3%”. It was concluded that the proposed methodology provides highly satisfactory results in the detection of anomalous soil moisture records, in order to make adjustments to the environmental conditions of Colombia.

Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean ( Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government’s SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected.