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In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)'s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD's SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD's SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region. [ABSTRACT FROM AUTHOR]

The "Green Revolution" (GR) technology-induced agricultural intensification has transformed India from food scarcity to a food surplus country. However, this has also resulted into several adverse repercussions. Increased application of chemical fertilizers and pesticides with stagnating/declining crop productivity has dovetailed with uncertain market conditions and climate change effects which has resulted in un-remunerative agriculture. Consequently, farmers have fallen into the debt trap due to the rising cost of crop production apart from health hazards due to serious exposure to harmful chemical pesticides. Natural Farming (NF), an agro-ecological approach to farming is believed to be an effective way to counter some of these challenges. The present paper presents field-level farmers' experiences of NF adoption in three states of India—Andhra Pradesh, Karnataka, and Maharashtra. The study was conducted during February–March 2019 by surveying 295 NF adopted and 170 non-NF adopted farmers. It was found that NF practice has been followed by some farmers for more than 10 years but others have adopted during the recent past. There is variation in the practice followed by the NF farmers. There are farmers who are using Farm Yard Manure (FYM). A solid form of jeevamritha (liquid concoction of microbial inoculants) called as ghanajeevamritha was also found to be used by farmers in Andhra Pradesh. It was observed that non-NF yields are superior to NF yield without FYM. In most crops, however, NF with FYM had a greater yield than NF without FYM and non-NF farms. There has been a decrease in the variable cost and a marginal increase in the market price of NF produce. The study suggests that natural farming may be seen as one of the alternative practices which has potential to rejuvenate the agro-ecosystem, besides cost saving for the individual farmers. [ABSTRACT FROM AUTHOR]

Wetlands are dynamic ecosystems that provide feeding and nesting grounds for diverse species of waterbirds. The quality of wetland habitat may have an impact on the density, diversity, and species richness of waterbirds. Toxic metal contamination is one of the most significant threats to wetland habitats. Feathers are a key indicator of heavy metal contamination in avian communities as a non-invasive method. We examined the levels of Arsenic (As), Cadmium (Cd), Cobalt (Co), Chromium (Cr), Copper (Cu), Lead (Pb), Nickel (Ni), and Zinc (Zn) using ICP-AAS and standards of digestion procedure from the primary feathers of 10 distinct species of waterbirds. The study was conducted at four wetlands, viz., Point Calimere Wildlife Sanctuary (Ramsar site); Pallikaranai Marshland (Ramsar site); Perunthottam freshwater lake (unprotected wetland), Tamil Nadu and the Pulicat Lake, Andhra Pradesh, (Ramsar site), India. The Large crested tern had higher concentrations of As, Co, Cr, and Ni. Cu was greater in the Indian pond heron, and Zn was higher in the Grey heron. The accumulation of metals differed among the waterbirds (p < 0.05), and the inter-correlation of metals found positive influences between the tested metals, i.e., Co was positively associated with As, Cr had a positive correlation with As and Co, and Ni was positively correlated with As, Co, Cr, and Cu. In contrast, Pb had a positive association with Cu and Ni. The Zn was associated with Co, Cr and Cu. The level of metals in waterbirds was Zn > Cu > Cr > Ni > Pb > Co > Cd > As. The results showed that metal levels in the primary feathers of waterbirds were greater than the other species of waterbirds examined across the world. Thus, the study emphasizes that managing wetlands and controlling pollution is crucial to saving waterbirds; otherwise, the population and diversity of waterbirds will decline and become a significant threat to waterbird communities. [ABSTRACT FROM AUTHOR]

The demand for preserving existing roadway infrastructure has been increasing to regulate expensive reconstruction activities. The maintenance of homogeneous road sections is one of the approaches to economize the overall management of pavement systems. The existing homogeneous delineation methods consider one or two parameters for segmenting the pavements based on similar characteristics, which are found to be a repetitive process. Also, there is a need to consider multiple parameters that represent the functional, structural, and traffic characteristics in segmentation process. Therefore, the objective of this study was to develop a multi-parameter-based delineation approach (MPDA) to segment the pavements into subsections with similar features considering functional, structural, and traffic characteristics. Deflection bowl parameters, unified pavement health index (functional performance metric), surface layer modulus, and traffic reported in terms of AADT were employed for developing a multi-parametric delineation index (MPDI). A total of 1781 datapoints covering 26 road sections in the State of Andhra Pradesh, India, were used. The C-charts method-based segmentation for MPDI was applied to obtain the homogeneous sections. The devised approach was found to be efficient in segmenting the pavements as well as robust in selecting suitable maintenance strategies for each group of the homogeneous sections. Further, the segmentation processes were automated for easier implementation by the agencies. [ABSTRACT FROM AUTHOR]

Acreage estimates are crucial for forecasting menthol mint acreage, as crop output figures fluctuate from year to year in response to fluctuations in the market price of menthol mint oil. Thus, there are yearly fluctuations in the maximum price that farmers can obtain. Since low production arises from low rates, and high production results from high prices, these acreage estimate studies may be useful in lowering uncertainty regarding menthol mints' output. The widespread adoption of remote sensing technologies for assessing crop acreage at both the national and international levels can be attributed to their low cost, ease of use, and flexibility. The extent of an area planted with menthol mint in the Vishakhapatnam district of Andhra Pradesh, India, was estimated using Sentinel-2A satellite data for that year. After conducting a comprehensive ground survey, the area of the menthol mint crop was estimated using an adaptive maximum chance-type set of rules for taluk-level statistics. According to the research, the Bheemunipatnam taluk in the Vishakhapatnam district was the most productive in growing menthol mint. Using customer and manufacturer accuracies of 89.13% and 87.23%, along with the average accuracy (90.67%) and kappa rate (0.9), the total acreage of menthol mint crop in the study region was estimated to be around 58,000,284.70 ha (0.844). A further aim in this study was to estimate the acreage planted with early and late menthol mint. Around 26,123.50 ha and 29,911.40 ha were found to be home to early menthol mint and late menthol mint, respectively. This method shows promise for early- and late-stage crop acreage assessment of menthol mint using a localised degree of precision. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is adversely impacting food and nutrition security and requires urgent attention from policymakers. Sustainable intensification of agriculture is one strategy that attempts to increase food production without adversely impacting the environment, by shifting from water-intensive crops to other climate-resistant and nutritious crops. This paper focuses on the Indian state of Andhra Pradesh by studying the impact of shifting 20% of the area under paddy and cotton cultivation to other crops like millets and pulses. Using FAO's CROPWAT model, along with monsoon forecasts and detailed agricultural data, we simulate the crop water requirements across the study area. We simulate a business-as-usual base case and compare it to multiple crop diversification strategies using various parameters—food, calories, protein production, as well as groundwater and energy consumption. Results from this study indicate that reduced paddy cultivation decreases groundwater and energy consumption by around 9–10%, and a calorie deficit between 4 and 8%—making up this calorie deficit requires a 20–30% improvement in the yields of millets and pulses. We also propose policy interventions to incentivize the cultivation of nutritious and climate-resistant crops as a sustainable strategy towards strengthening food and nutrition security while lowering the environmental footprint of food production. [ABSTRACT FROM AUTHOR]

Mangrove forest is one of the productive ecosystems that provide essential habitats for various fauna as breeding and feeding drives. However, heavy metal pollution in the mangrove forest has led to severe health problems for several aquatic species. Biomonitoring of metals using a nondestructive method is an emerging technique. Scats of the fishing cat (Prionailurus viverrinus) were collected from five locations in the Godavari estuary mangrove habitats, Coringa Wildlife Sanctuary, Andhra Pradesh, India, to determine the level of various metals. An opportunistic method was applied to collect scats in the mangrove forest. Six scat samples were collected from each of the sampling sites. The following prey species, such as crabs, fishes, birds, rodents, plants, plastics, and unidentifiable prey matters, were found in the scats. Select metals, such as chromium (Cr), copper (Cu), and lead (Pb) were analyzed from the scats of the fishing cat since they intensively influence the physiology and behavior of top predators. The concentration of Cu in fishing cat scats was higher than the other two metals assessed. Metals showed statistically substantial variation across locations (p < 0.05). According to the current study, heavy metals may significantly threaten the fishing cat in the Coringa Wildlife Sanctuary. The fishing cat is a vulnerable species in accordance with the ICUN categories. Due to pollution and other human pressures, the fishing cat may soon be categorized as a threatened or endangered species; the research advises that authorities should prioritize the protection of the vulnerable species of the fishing cat from the Coringa Wildlife Sanctuary, Andhra Pradesh, India. [ABSTRACT FROM AUTHOR]

African oil palm (Elaeis guineensis Jacq.) is a bulk feeder of nutrients. In this study, we aimed at devising strategies for efficient nutrient management in the oil palm plantations of the Krishna River basin located in Andhra Pradesh, India by assessing soil fertility status, establishing optimal leaf nutrient concentrations and identifying yield restrictive nutrients. In total, 67 oil palm plantations were surveyed from this area in 2020, soil samples were collected and analysed for different soil properties, including pH, EC, SOC, available P, K, S, exchangeable Ca and Mg, and hot water-soluble boron (HWB) in surface (from 0–20 cm depth), subsurface (from 20–40 cm depth) and deep (from 40–60 cm depth) soil layers. As per DRIS (Diagnosis and Recommendation Integrated System) indices estimated in this study, the order of requirement of nutrients is Nitrogen (N) > B > K > P > Mg for this area. Optimum leaf nutrient concentrations ranged between 2.07–4.29%, 0.13–0.27%, 0.52–0.94%, 0.44–0.76% and 44.97–102.70 mg/kg for N, P, K, Mg and B, respectively. In surveyed plantations, about 15, 6, 16, 9 and 12 percent of leaf samples had less than optimum concentration of N, P, K, Mg and B respectively. Nitrogen and Boron are the major yield limiting factors in this region. Leaf nutrient concentrations need to be maintained at the optimum ranges as estimated above for higher productivity in the Krishna basin area. [ABSTRACT FROM AUTHOR]

Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction helps market pricing, planning labor, transport, and harvest organization. The main aim of this study is to predict crop yield accurately. The incorporation of deep learning models along with crop statistics can predict yield rates accurately. We proposed an improved optimizer function (IOF) to get an accurate prediction and implemented the proposed IOF with the long short-term memory (LSTM) model. Manual data was collected between 1901 and 2000 from local agricultural departments for training, and from 2001 to 2020 from government websites of Andhra Pradesh (India) for testing purposes. The proposed model is compared with eight standard methods of learning, and outcomes revealed that the training error is small with the proposed IOF as it handles the underfitting and overfitting issues. The performance metrics used to compare the loss after implementing the proposed IOF were r, RMSE, and MAE, and the achieved results are r of 0.48, RMSE of 2.19, and MAE of 25.4. The evaluation was performed between the predicted crop yield and the actual yield and was measured in RMSE (kg/ha). The results show that the proposed IOF in LSTM has the advantage of crop yield prediction with accurate prediction. The reduction of RMSE for the proposed model indicates that the proposed IOFLSTM can outperform the CNN, RNN, and LSTM in crop yield prediction. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman's rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen's slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, "modifiedmk" and "trendchange" to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region. [ABSTRACT FROM AUTHOR]

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 2017–2019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and "other" crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems. [ABSTRACT FROM AUTHOR]