Showing papers in "Journal of Agrometeorology in 2023"
TL;DR: In this article , the authors evaluated the correlation between fluctuations in the gas-of-interest's mixing ratio and the vertical wind velocity and considered the most accurate approach for measuring gas fluxes, mostly carbon dioxide and water vapor under ideal homogeneous conditions.
Abstract: Eddy correlation measures gas exchange between canopy and the overlying atmosphere by evaluating the correlation between fluctuations in the gas-of-interest’s mixing ratio and the vertical wind velocity and considered the most accurate approach for measuring gas fluxes, mostly carbon dioxide and water vapor under ideal homogeneous conditions. It has been used in micrometeorology for decades to quantify mass and energy transfer between urban, natural and agricultural ecosystems and the atmosphere. We assessed its application under various—homogeneous and non-homogeneous—conditions. Our study indicates that fluxes of CO2 and H2O correlate well with plants activity only when turbulent conditions are present, in open fields. At such conditions, direct measurements of concentration of those gases are not an accurate indicator for plants activity. On the other hand, in closed systems (e.g. greenhouses)- fluxes as measured by an eddy correlation system can't accurately be related to the state of the vegetation, but the fluctuations in the concentrations of CO2 and H2O directly correlate to the actual plants activity. Adapting conditions in greenhouses to limiting factors as temperature, increases CO2 sequestration by plants, and may increase productivity
TL;DR: In this article , the authors explored agricultural applications of sub-seasonal forecasts on agricultural management in India and showed how the extended range weather forecast has been developed and translated into agromet advisories for the farming communities to increase crop production in India.
Abstract: Under the climatic variability and climate change, skilful weather forecast in different spatial and temporal scale encourages the farmers to organize and activate their own resources in the best possible way to increase the crop production. Though medium range weather forecast is used extensively in operational agromet advisory services, sub-seasonal forecast provides additional decision-relevant information to support the timing of crop planting, irrigation scheduling, and harvesting, particularly in water-stressed regions. In view of that, dynamical and statistical and sub-seasonal seasonal forecast is generated and delivered to the farmers as climate information services in number of countries in the world. Under the Gramin Krishi Mausam Sewa (GKMS) Project, Agricultural Meteorology Division, India Meteorological Department (IMD) in collaboration with Indian Institute of Tropical Meteorology (IITM), Pune, All India Coordinated Research Project on Agrometeorology (AICRPAM), CRIDA, Indian Council of Agricultural Research, Hyderabad prepared Agromet Advisory fortnightly taking into consideration realized rainfall during previous fortnight and extended range rainfall forecast for next fortnight and crop information i.e., state and stage of the crops. In the present article agricultural applications of sub-seasonal forecasts on agricultural management in India has been explored. It has been showed how the extended range weather forecast i.e., sub-seasonal forecast has been developed and translated into agromet advisories for the farming communities to increase crop production in India and whether the present state of accuracy could be used for generating advisory under contingent crop planning conditions and other advisories by citing different case studies and ultimately helping the farming communities to improve their economic conditions. It has been demonstrated here that sub-seasonal forecasts are increasingly being used across agriculture in the country. The sub-seasonal forecasting time scale is therefore a new concept for many users. Because of the additional value of sub-seasonal forecasts for decision-making, it is increasingly gaining interest among users. Present case studies clearly suggest the forecast at sub-seasonal time scale is need of the hour.
TL;DR: In this article , the use of a time-delay wavelet neural network (TDWNN) was proposed to predict rainfall in an agriculturally dependent country like India, which employs a TDNN architecture with a hidden layer activation function derived from the orthonormal wavelet family.
Abstract: In an agriculturally dependent nation like India, accurate and effective rainfall forecasting methods are crucial for assessing agrometeorological risk. Forecasting rainfall is perhaps one amongst the most arduous tasks in this context due to the prevalence of a non-linear pattern. One of the most promising and frequently employed approaches for forecasting rainfall data is the Time-Delay Neural Network (TDNN) model. TDNN's non-parametric, data-driven, and self-adaptive characteristics make it increasingly attractive for modelling nonlinear dynamics and generating nonlinear forecasts. Nevertheless, since the conventional TDNN uses the sigmoid activation function, there is always a chance that the training process may converge to local minima. This study addresses the usage of a Time-delay wavelet neural network (TDWNN), which employs a TDNN architecture with a hidden layer activation function derived from the orthonormal wavelet family, in order to circumvent this issue. TDWNN has been empirically demonstrated using annual rainfall data from two districts in the Indian state of West Bengal. According to the findings of this study, the TDWNN model is superior than the conventional TDNN method to assess agrometeorological risk.
TL;DR: In this paper , a study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) for semi-arid region of Haryana state.
Abstract: The study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) for semi-arid region of Haryana state. Ten years (2011-2020) daily weather data of maximum and minimum temperature, relative humidity, wind speed and sun shine hours was collected from the meteorological observatory at CCS HAU, Hisar. Multilayer perceptron feed forward back propagation ANN models were evaluated for different training algorithms (10), number of hidden layers (1-3) and number of neurons in hidden layers (1-30). Training algorithms compared in the study were heuristic techniques (GDA, GDX, RP), conjugate gradient (CGF, CGP, CGB, SCG), quasi-Newton (BFG, OSS) and Levenberg-Marquardt (LM). Results were compared against standard FAO Penman-Monteith method. The study revealed that best performance for ANN was found with LM algorithm in single layer of 13 neurons exhibiting RMSE, R, ME and RPD values 0.306, 0.986, 0.976 and 6.63, respectively. ANN models showed good performance in prediction of reference evapotranspiration.
TL;DR: In this paper , the uncertainties associated with ET methods and input data, uncertainties due to spatial and temporal scales, and uncertainties based on region were identified, and five ways to minimize uncertainties in ET estimations were identified.
Abstract: Accurate estimation of evapotranspiration (ET) is essential both at the regional and local scales for many management tasks. Numerous methods for estimating ET with various complexities and combinations exist which may be broadly classified as direct and indirect methods. Information on ET estimation uncertainties cannot be overemphasized and ignoring them can misguide decision-making in management of water resources. This study reviews the uncertainties in ET estimations and suggests ways to reduce them. Identified in this study are uncertainties associated with ET methods and input data, uncertainties due to spatial and temporal scales, and uncertainties based on region. Many studies have the ET method related uncertainties. The ground-based techniques generally used as a standard for comparing other methods have considerable uncertainty (10–30%) associated with the input components. The errors from the input reflect in the estimated ET output irrespective of the model used. Datasets from satellite products are based on in-situ network forcing as well as on model’s estimation and remote sensing (RS), and they are prone to errors as a result of differences in in-situ measurements, scale, sensor calibration and basics of model theory and parametrization. Generally, uncertainties associated with ET were found to vary temporally. Also, homogeneity and stability of potential evapotranspiration (PET) were worse in space than in time, indicating that the temporal distribution of PET was more uniform and stable compared to spatial distribution. Some ET RS products showed less uncertainty in coarse resolution and comparatively high uncertainty in fine resolution. This study identified five ways to minimize uncertainties in ET estimations. Minimizing uncertainty in ET estimation will definitely improve planning, management and use of water resources especially where accurate estimations are required.
TL;DR: In this paper , the field experiments related to the study were conducted at Punjab Agricultural University, Ludhiana and Krishi Vigyan Kendra, Bahowal, Punjab.
Abstract: The field experiments related to the study were conducted at Punjab Agricultural University, Ludhiana and Krishi Vigyan Kendra, Bahowal. There were 20 treatment combinations comprising of four levels of sunlight intensity [control (full sunlight), 50% reduction in sunlight light intensity during 15-45(R15-45), 46-75(R46-75) and 76-105(R76-105) DAT] and five levels of foliar nitrogen application [control (only recommended nitrogen, no foliar spray), spray of 3% urea before (NB), midway(NM), afterwards(NA) and midway-afterwards(NMA) the reduction in sunlight intensity in addition to the recommended nitrogen application]. Radiation level of R15-45 significantly preponed (2-5 days) the occurrence of phenological stages from maximum tillering to physiological maturity. Similarly, R46-75 significantly preponed (3-9 days) the occurrences of flag leaf initiation to physiological maturity. The nitrogen level of NB, significantly delayed the occurrence of flag leaf initiation to physiological maturity stages (2-5 days), NM delayed flag leaf initiation to physiological maturity (2-4 days), NA delayed anthesis to physiological maturity (1-4 days) and NMA delayed panicle emergence to physiological maturity (2-5 days). Plant height of rice was significantly increased due to R15-45 (5.21-5.26%) and R46-75 (6.95-7.61%) and also with foliar nitrogen application levels of NB, NM, NA and NMA by 9.95-9.89, 9.65-10.06, 8.06-7.57 and 9.65-9.29 per cent, respectively. Number of tillers m-2 was significantly reduced (4.71-15.99%) under low light conditions and was increased (5.59-23.34%) with foliar nitrogen application. Yield attributes were affected by the reduced sunlight intensity and foliar nitrogen application. Grain yield was significantly reduced as a result of R15-45 (9.19-11.41%), R46-75 (14.29-16.35%) and R76-105 (8.15-10.67%) and foliar nitrogen application levels of NB, NM and NMA resulted in significant increase the grain yield by 9.32-10.19, 5.74-6.38 and 4.59-5.74 per cent respectively, as compared to control.
TL;DR: In this article , the authors have suggested limited management solutions have been suggested against sun stroke, depression, pollution induced asthma, infertility, skin disorders, chronic kidney disease, etc., which are available across literature and also found presently at ground level practice.
Abstract: Under severe global warming scenario, people, especially farmers would face chronic climate torments like, sun stroke, depression, pollution induced asthma, infertility, skin disorders, chronic kidney disease etc., since their main activity comes under open field conditions. If the climate stress continues beyond threshold level, it may lead to social disturbance, mental disorders, suicides etc. Psychological treatment combined with pharmaceutical treatment would serve better to release stress from climate related disorders. Limited management solutions have been suggested against sun stroke, depression, pollution induced asthma, infertility, skin disorders, chronic kidney disease. Still, many viable solutions are available across literature and also found presently at ground level practice. This area needs further intensive team research among medical, meteorological, and social scientists. Both Central and State Governments must enact strong policy on this line, if not done earlier. In addition, action must be taken by all the global countries to reduce greenhouse gas emission as per the Paris CoP agreement,2015.
TL;DR: In this article , soybean (Glycine Max L) based cropping systems (sole crop, and intercropped with cotton or pigeon pea) through different combinations of cultivation practices (flatbed, raised bed) and irrigation levels (Rainfed, 66%ETc, 100% ETc and methods (drip, sprinkler) were studied in randomized block design with three replications during kharif season of 2019-20 and 2020-21.
Abstract: Building resilience to climate change through on farm management techniques such as crop diversification, and water management as supplemental irrigation is vital for sustainable agriculture. In the present study, soybean (Glycine Max L.) based cropping systems (sole crop, and intercropped with cotton or pigeon pea) through different combinations of cultivation practices (flatbed, raised bed) and irrigation levels (Rainfed, 66%ETc, 100%ETc and methods (drip, sprinkler) were studied in randomized block design with three replications during kharif season of 2019-20 and 2020-21. Plant growth parameters viz. plant height and dry weight were recorded maximum in rainfed soybean as sole crop, while the number of branches/plant were recorded maximum in sole soybean crop irrigated at 100%ETc. Grain yield (5.37 t ha-1), and water productivity (0.47 kg m-3) were maximum in soybean intercropped with cotton. Overall, cotton+soybean irrigated at 66% ETc can be adopted by farmers to achieve optimal productivity without significant yield penalty.
TL;DR: In this paper , the authors investigate six factors that influence oil palm yield in Peninsular Malaysia, including mean annual minimum and maximum temperatures, mean annual rainfall, average number of rainy days per year, average annual relative humidity, and elevation.
Abstract: The objective of this study is to model oil palm yield distributions and investigate the factors that influence oil palm yields in Peninsular Malaysia using remote sensing and geographical information system (GIS) techniques. Herein, we investigate six factors that influence oil palm yield in Peninsular Malaysia, including mean annual minimum and maximum temperatures, mean annual rainfall, average number of rainy days per year, average annual relative humidity, and elevation. In order to model oil palm yield in Peninsular Malaysia, a large yield dataset covering Peninsular Malaysia for 37 years (1983 to 2020), as well as related explanatory variables, were collected. Areal interpolation was used to model the average yield distribution across the study area. The findings of this study show that oil palm yields vary across Peninsular Malaysia. Due to favourable climate and elevation, southern and southwestern Peninsular Malaysia, including Johor, Pahang, Melaka, and Selangor, recorded the highest amount of yield.
TL;DR: In this paper , the role of forest belts of a combined structure on the characteristics of snow deposition depending on different patterns of shrub placement (along the top edge, along the lower edge, on both sides).
Abstract: In semi-arid climate conditions, where farming is complicated by a lack of atmospheric moisture, the preservation of snow in an agroforest landscape serves as an additional source of moisture for the growth and development of tree and shrub vegetation. The paper investigates the role of forest belts of a combined structure on the characteristics of snow deposition depending on different patterns of shrub placement (along the top edge, along the lower edge, on both sides). The results of the conducted snow surveys show that experimental sites with shrubs along the top edge are characterized by the highest level of snow accumulation both in the forest belt and in the adjacent field. The snow-retaining function in the forest belt zone is weaker in the presence of shrubs on both sides. It has been established that the values of snow density increase with approaching the forest stand. The highest values were recorded in the forest belt with shrubs along the top edge (up to 0.5 g cm-3). The accumulation of snow and its density eventually affected the amount of snow reserves. The highest values of snow reserves were observed in the forest belt with shrubs along the top edge with a row width of up to 1 m. This contributed to the accumulation of 82-203 mm of snow in the forest belt area (at 43 mm of snowfall). Shrub placement along the lower edge provoked a loss of moisture in the forest belt itself, which made this pattern ineffective. The results obtained can be applied in the design of protective forest belts in the areas with insufficient moisture.
TL;DR: In this paper , an Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS, which includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc.
Abstract: India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) in collaboration with Indian Council of Agriculture Research (ICAR), State Agriculture Universities (SAUs) , Indian Institute of Technology (IITs) and other organizations is rendering weather forecast based District level Agrometeorological Advisory Service (AAS) for benefits of farmers in the country under the centrally sponsored scheme ‘Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) ’ of MOES. AAS, popularly known as Gramin Krishi Mausam Sewa (GKMS) provides advance weather information along, with crop specific agromet advisories to the farming community by using state of the art instruments and technology through efficient delivering mechanism of the information which ultimately enables farmers to take appropriate actions at farm level. The various components of GKMS viz. observing weather, its monitoring and forecast; crop specific advisory bulletin generation and dissemination; outreach and feedback have been/are being digitized to support integrating all the components of information generation and action suggested linked to these information. An Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS. This includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc. to translate weather forecast into actionable farm advisories for efficient farm level decision making in India. Apart from this, effort is being made to develop recent technology driven tools to estimate future yield of crops and prepare an irrigation schedule without a need of multiple parameters.
TL;DR: In this article , the authors quantified the future climate suitability of aroids for future climate scenarios 2030, 2050, and 2070 for the two representative concentration pathways (RCP 4.5 and RCP 8.5).
Abstract: Elephant foot yam and taro are the two important aroids of tropical tuber crops, considered as underutilized in the context of climate change and food security. The present study focused to quantify the future climate suitability of aroids for future climate scenarios 2030, 2050, and 2070 for the two representative concentration pathways (RCP 4.5 and RCP 8.5). The district-wise future climate suitability of elephant foot yam and taro using MaxEnt across India was quantified. The percentage increase in climatically suitable area for taro is 49% and the same for elephant foot yam is 46% which is higher compared to those of tropical root crops. A total of 218 districts were identified as highly suitable for the cultivation of elephant foot yam for different RCPs across India. A total of 209 districts were observed as highly suitable for taro cultivation across India for the two RCPs. The information about the districtlevel suitability can assist decision-makers to understand the possible shifts in the climate suitability of aroids in India in the context of food security as they have higher productivity compared to other major food grain crops.
TL;DR: In this article , a field trial was conducted during rabi 2019-20 and 2020-21 at Punjab Agricultural University, Ludhiana, Punjab, India to assess the effect of terminal heat stress on the in vitro screened heat tolerant and susceptible genotypes of barley.
Abstract: In order to assess the effect of terminal heat stress on the in vitro screened heat tolerant (n=9) and susceptible (n=3) genotypes of barley, a field trial was conducted during rabi 2019-20 and 2020-21 at Punjab Agricultural University, Ludhiana, Punjab, India. Barley genotypes were sown under timely (November 26) and late sown (December 26) conditions so that late sown crop encounters heat stress during its reproductive stages of growth. The results showed that timely sown crop took significantly higher number of days to attain physiological maturity as compared to late sown crop. For anthesis and physiological maturity, timely sown crop accumulated higher growing degree days (GDD) in comparison to late sown crop. Tolerant genotypes (viz., BL 1515, BL 1729, BL 1780, BL 1784, BL 1786, BL 1792, BL 1794, BL 1797 and IBYT-E24) recorded higher number of GDD for attaining physiological maturity in comparison to susceptible genotypes (viz., BL 1723, IBON 23 and IBYT-E15) under late sown conditions. Likewise, heat use efficiency (HUE) was also lower in susceptible genotypes as compared to tolerant genotypes particularly under late sown conditions. Results also indicated that under timely sown conditions, grain yield of tolerant genotypes was statistically at par to susceptible genotypes; but under late sown conditions, tolerant genotypes out yielded susceptible genotypes. Among the tolerant genotypes, BL1786 had the highest grain yield under late sown conditions and it was statistically similar to three other tolerant genotypes namely BL1780, BL1784 and BL1792. Tolerant genotypes recorded lower tolerance index (TOL) and stress susceptibility index (SSI) values in comparison to susceptible genotypes; however, exhibited higher values of yield stability index (YSI). Correlation studies indicated that number of days taken to physiological maturity is the most crucial phenological stage determining seed yield of barley under late sown conditions.
TL;DR: In this article , the use of space-based spectral information with weather inputs for wheat yield modeling by empirical and crop simulation models is reviewed and extended with enhanced spectral modeling approach for districts in central Punjab.
Abstract: Use of space-based spectral information with weather inputs for wheat yield modeling by empirical and crop simulation models is reviewed and extended with enhanced spectral modeling approach for districts in central Punjab. The study uses multi-date and multi-year MODIS data at 250m resolution to both identify wheat crop and develop temporal spectral Enhanced Vegetation Index (EVI) profile for 2001-2019 period. Recently developed high resolution (12km) gridded temperature data from NCMRWF, namely India Monsoon Data Assimilation and Analysis (IMDAA) has been used for computing district-level average of daily (AV) and night-time (NT) temperatures. Multiple linear regression analysis with statistical tests on significance of coefficients for various inputs is used to investigate significance of various input parameters in yield models on multi-district data set. Results identify both area under spectral profile (AS-EVI) and mean peak value (PK-EVI) have significant control on yield. Individual district-level trend based yield (YT) is a significant coefficient in the multi-district models. Model performance is significantly improved by the inclusion of phase-specific temperatures and at specific post-flowering phases the night temperature figured in best models. Significance of the results in development of spatially resolved yields for applications like yield forecast, crop insurance and climate change studies is discussed.
TL;DR: In this article , the authors analyzed land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India.
Abstract: The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 < -0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories.
TL;DR: In this paper , a water-food-land nexus system is proposed to increase irrigation water production and optimise the allocation of scarce resources in the land-water-food nexus.
Abstract: Land, water, and food resources are essential for human survival, economic development, and social stability. Water and land are the basic resources in irrigated agricultural systems. Sustainable agricultural development entails effective management of the land-water-food nexus. The complex relationship in land-water-food nexus, with large uncertainties encompassed therein. Approaches that interconnect Land-Water-Food have grown significantly in scope and intricacy. In evaluating solutions to accomplish Sustainable Development Goals (SDGs) under the contexts of rising demands, resource paucity, and climate change, nexus techniques are helpful. The nexus analysis includes the important interlinkages that could be addressed. In the water-food-land nexus system, optimization approach can increase irrigation water production and optimise the allocation of scarce resources. Model optimisation considers the uncertainties in the systems to help decision-makers devise effective strategies for allocating water and land resources effectively. Integrated modelling with efficient optimization methods aid in solving real-world nexus management issues and provides the results that could serve as the basis for effective management of land-water-food nexus and formulation of agricultural policies.
TL;DR: In this article , the authors proposed a fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications.
Abstract: Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. HHaJathrough Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate.
TL;DR: The All India Coordinated Research Project on Agrometeorology (AICRPAM) was initiated in 1983 to utilize the climatic resource potential for better agricultural planning, enhanced productivity, profitability and sustainable livelihoods as discussed by the authors .
Abstract: The All India Coordinated Research Project on Agrometeorology (AICRPAM) was initiated in 1983 to utilize the climatic resource potential for better agricultural planning, enhanced productivity, profitability and sustainable livelihoods. The project has generated valuable research output in the areas of agroclimatic characterization, crop-weather relationship and weather effects on pests and diseases. Such information has been used for developing crop weather calendars, agroclimatic atlases, decision support systems, android apps, software for agromet data analysis, weather-based pest forewarning models, weather triggers for crop insurance etc. These products are being used for preparing agromet advisories and weather-related risk management systems. AICRPAM has completed forty years of its very meaningful existence with significant achievements and recommendations of practical value for the benefit of various stakeholders, particularly farmers. However, in view of the increase in intensity and frequency of the extreme weather events such as heat and cold waves, floods and droughts etc. under changing climatic conditions, the coordinated project envisages characterizing and identifying the hotspots, to minimize risks in crop production.
TL;DR: In this paper , the long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data.
Abstract: The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW–39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of co-efficient of variation (CV) showed low CV (<60%) in Pd and high CV (>60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (<40%) in Pw. The drought prone and moisture sufficient zones were indentified based on the analysis of long-term frequency distribution of dry-wet spells and trend. Inter-comparison of IPs between CHIRPs with IMD (Indian Meteorological Department) and NOAA CPC (National Oceanic and Atmospheric Administration/Climate Prediction Centre) showed encouraging results. The study provides baseline reference for climate-resilient agricultural crop planning with respect to food security.
TL;DR: A field investigation was made at the Tamil Nadu Agricultural University farm in Coimbatore during the late Kharif 2019 and late Rabi 2019-20 seasons to quantify the impact of induced moisture stress (MS) at critical stages (10, 15, 20, and 25 days from panicle initiation and flowering) on physiological traits and yield of rice as discussed by the authors .
Abstract: A field investigation was made at the Tamil Nadu Agricultural University farm in Coimbatore during the late Kharif 2019 and late Rabi 2019-20 seasons to quantify the impact of induced moisture stress (MS) at critical stages (10, 15, 20, & 25 days from panicle initiation and flowering) on physiological traits and yield of rice. The experiment was laid-out in randomized complete block design (RCBD) with three replications. During both seasons, physiological traits (photosynthetic rate, stomatal conductance, transpiration rate and chlorophyll index) were recorded after the MS period (10, 15, 20 and 25 days) at both critical stages. The experimental results revealed that MS of any period and any stage (panicle initiation and flowering) reduced the values of all physiological traits, grain and straw yields in both seasons. The MS period of 25 days from panicle initiation significantly reduced all physiological parameters, including rice yield.
TL;DR: In this paper , the authors analyzed the spatio-temporal variation of the crop water requirement (ETc), crop water surplus deficit index (CWSDI), and the coupling degree of ETc and effective precipitation (Pe) for maize (Zea mays L.) crop grown in Haryana State of India using ArcMap 10.8 software.
Abstract: Climate change have a considerable impact on crop water demand (ETc). The present study analyzed the spatio-temporal variation of the crop water requirement (ETc), crop water surplus deficit index (CWSDI), and the coupling degree of ETc and effective precipitation (Pe) for maize (Zea mays L.) crop grown in Haryana State of India using ArcMap 10.8 software. ETc was calculated using the Penman-Monteith method and crop coefficient (Kc) approach for 34 years (1985-2018) climatic data. Two statistical models, namely Mann–Kendall test and Sen’s slope estimator, were applied to understand the trend, while Pettitt's test was used to identify abrupt change points by using XLSTAT 2021 software.The result showed that for maize ETc ranged from 571 to 766 mm, CWSDI ranged from -29 to -74% and the degree of coupling of ETc and Pe ranged from 0.26 to 0.71 during 1985-2018. The decadal temporal variation of these indices indicated that ETc during 2007-2018 was relatively higher than it was from 1985-1995 and 1996-2006. The spatio-temporal values of CWSDI indicated that almost all the districts were under water deficit conditions for growing maize crop.Trend analysis showed significantly increasing trend of CWR for most of the districts except Jind, Mewat, Palwal, Panchkula, Rewari, Rohtak and Yamunanagar.The abrupt change point detection analysis of CWR indicates toward different change points, with maximum change points between the years 2004–2008.Such information will help to the farmers in the effective management of water for sustainable production under climate change in the near future.
TL;DR: In this article , the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries.
Abstract: Increased amount of green house gases (GHG) in the atmosphere will cause climate change that will adversely impact crop production especially in the arid and semi-arid regions of the developing countries. Development and implementation of field level adaptations measures to cope up with climate change are necessary to the farmers whose livelihood depends on crop-based income. Crop simulation models that incorporate soil-crop-climate processes of plant growth and that are sensitive to climate change factors can be used to quantify impact of climate change on crop production and evaluating and prioritizing adaptation measures at farm level. This paper analyses the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries. The crop models need to be linked to the improved pest, disease and weed models to analyze and predict yield losses, especially those due to climate change. The simulation models also need to incorporate the impact of extreme weather events on crop production that is projected to increase with climate change.
TL;DR: In this paper , the authors have proved the pathogenecity of R. bataticola was proved and the identity of pathogen was confirmed molecularly using ITS-1 and ITS-4 primers which produced amplified product size of 500-650 bp in three studied isolates indicating that all the isolates belonged to genus R. Bataticola.
Abstract: Chickpea is one of the most important food legumes being cultivated in many countries in the world. Dry root rot caused by Rhizoctonia bataticola is becoming an emerging disease and considered as potential threat to chickpea productivity and production under changing climatic scenario. The pathogenecity of R. bataticola was proved and the identity of pathogen was confirmed molecularly using ITS-1 and ITS-4 primers which produced amplified product size of 500-650 bp in three studied isolates indicating that all the isolates belonged to genus R. bataticola. The maximum colony growth of pathogen and the dry root rot disease severity was recorded at 30-35ºC which is considered as optimum temperature range for growth of pathogen and development of disease. Highest severity of dry root rot and lesser plant growth parameters such as root length, shoot length and total biomass were observed at 40-60% soil moisture regimes, irrespective of type of soil. The elevated CO2 @ 550 ± 25 ppm with 2ºC rise in temperature recorded higher dry root rot well as reduced growth parameters of chickpea. The increase in the temperature lead to decreased radial growth of pathogen and dry root rot incidence and increase in the soil moisture led to increase in growth parameters in both black as well as red soils.
TL;DR: In this article , different forecasting models like ARIMA, ARIMAX, SARIMAX and SVR have been fitted and validated using RMSE values, and the best fitted model was found to be the best fitting model followed by SVR and SARIMA.
Abstract: Jute crop cultivated in Cooch Behar suffers a substantial amount of physical and economical loss every year due to several major insect pest infestation such as Yellow Mite (Polyphagotarsonemus latus Banks) and Jute Semilooper (Anomis sabulifera Guen). Constructed seasonal plots reveal that for Yellow Mite pest incidence is maximum at 55 DAS, while for Jute Semi Looper it is at 45 DAS. Correlation analysis indicate that the weather parameters such as minimum temperature at current week, maximum RH at one week lag, minimum temperature, minimum and maximum RH at two week lag are significantly correlated with the incidence of Yellow Mite, while in case of Jute Semilooper maximum temperature, minimum and maximum RH at two week lag are significantly correlated. Different forecasting models like ARIMA, ARIMAX, SARIMA, SARIMAX and SVR have been fitted and validated using RMSE values. In case of Jute Semilooper, SARIMAX model is found to be the best fitted model followed by SVR and SARIMA. Similarly, for Yellow Mite ARIMAX model produces the least RMSE value followed by SVR and ARIMA. Following successful model validation, forecasting is done for the year 2022 using the best fitted models.
TL;DR: In this paper, an attempt was made for scheduling irrigation at a regional scale combining satellite data and agrometeorological indices over major rice growing tracts of Palakkad district in Kerala.
Abstract: The sustainability of irrigated agriculture is jeopardized by catastrophic climate change, with projected forecasts indicating that by 2025, one out of every four people on the planet will be experiencing extreme water scarcity. In this context, an attempt was made for scheduling irrigation at a regional scale combining satellite data and agrometeorological indices over major rice growing tracts of Palakkad district in Kerala. Normalized Difference Vegetation Index (NDVI) product of MODIS (MOD13Q1) with a temporal resolution of 16 days and a spatial resolution of 250 m was utilized to establish a relationship with crop coefficient (Kc) of rice during the mundakan rice season of 2020-21 and 2021-22 in 30 ground truth locations. The results revealed that NDVI values have strong relationship with Kc values with an R2 value of 0.81. Crop coefficient (Kc) maps developed using satellite derived NDVI provided Kc values at a regional scale during different stages of crop growth and it helped to estimate crop evapotranspiration with greater accuracy. Based on this crop water demands maps depicting the spatial and temporal distribution of irrigation requirement were generated for the whole study area. These maps can be used as a tool for the estimation of the crop water requirement of a rice field if the geographical coordinates of the location are known. The total crop water requirements estimated during mundakan season 2020-21 and 2021-22 in Palakkad district were in the range of 700-975 mm and 560-897 mm respectively. Integration of remote sensing & agrometeorological techniques has scope for regional-scale crop water requirement estimation in a cost-effective and time-bound manner.
TL;DR: In this paper , the authors analyzed the soil temperature data of two stations (Białystok and Suwałki) of Poland for 40 years (1981-2020) measured at five depths (5, 10, 20, 50, 100 cm) were analyzed.
Abstract: Global climate change is one of the factors changing the thermal regime of soils and thus the conditions of agricultural production. The soil temperature data of two stations (Białystok and Suwałki) of Poland for 40 years (1981-2020) measured at five depths (5, 10, 20, 50, 100 cm) were analyzed. The averaging of the soil temperature for the layers 0-20 cm and 0-50 cm indicated a progressive warming of soils. At the beginning of the 21st century, there was a change in the thermal regime from frigid to mesic, with an average rate of 0.40C per decade. In the summer months, rate of increase was twice as high. Soil climate change results in already changing structure of plant cultivation and the need to introduce new elements in the technology of soil cultivation. To ensure a satisfactory yield of plants, it will be particularly important to rationally modify the water management of agricultural areas.