Journal of Agrometeorology

Resilience of livestock production under varying climates

2022-12-16T15:23:21+00:00

The better adaptation and mitigation options can help to curtail the effects of climate change on livestock performance. To reduce poverty and promote sustainable development through livestock production, favorable policies and action-oriented research are urgently required to address the pertinent issue. For effective adaptation and mitigation measures to address climate change and livestock production, these measures should be scaled up through policy. For example, understanding farmers’ perceptions and including them in policy development can improve food security and environmental conservation by promoting widespread practice adoption. In addition, a comprehensive view of costs, time, and effort required from the producer need to be included to the policy framework to maintain sustainable and resilient production systems.

Eddy correlation measurements to visualize CO2 and water vapor concentrations and fluxes

2023-01-28T16:59:35+00:00

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

Assessing the long-term fluctuations in dry-wet spells over Indian region using Markov model in GEE cloud platform

2023-04-10T19:58:14+00:00

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 (23^rd SMW–39^th SMW) was identified where initial probabilities (IPs) of dry (P_d) and wet (P_w) 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 P_d and high CV (>60%) in P_w in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in P_d and low CV (<40%) in P_w. 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.

Future climate suitability of underutilized tropical tuber crops-‘Aroids’ in India

2023-03-20T03:33:28+00:00

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.

Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020

2023-04-06T22:20:16+00:00

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.

Effect of induced moisture stress at critical stages on physiological traits and yield of rice (Oryza sativa L.)

2023-03-14T04:24:50+00:00

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.

Selection of sensitive bands for assessing Alernaria blight diseased severity grades in mustard crops using hyperspectral reflectance

2023-02-10T01:46:30+00:00

Recent development in remote sensing technology using hyperspectral reflectance or spectroscopic data was enabled the rapid and ongoing progression of monitoring, mapping, and surveillance/detection of insects tools for better crop management. This study describes a spectroscopic based methodology to escalation the efficiency of present surveillance practices (insect traps and human examinations) for detection pest infestation (e.g., Alternaria blight in mustard crop). The methodology uses ground based hyperspectral data across the spectral bands 350-2500nm at 1 nm interval. Three different statistical procedures such as correlation (between reflectance, 1^st and 2^nd derivatives with diseased severity grades), continuum removal analysis was implemented for selection of sensitive bands. In this method, we explore the combinations of different selected sensitive spectral bands and regions to separate diseased crops. The objectives of this research is to develop a novel methodology for selection of sensitive bands to Alternaria blight diseased crops. The development of such methodology would provide researchers, Agronomist, and remote sensing practitioners reliable and stable method to achieve faster technique with higher accuracy to mapping of Alternaria blight diseased crops.

Assessment of AquaCrop model for simulating Baby corn (Zea mays L.) growth and productivity under different sowing windows and crop geometries

2023-02-09T19:39:11+00:00

The experiments were conducted at Agro Climate Research Centre, TNAU, Coimbatore. Calibration and validation of AquaCrop model was done using Winter and Kharif, 2022 data. Calibration showed that AquaCrop accurately simulated the canopy cover by low RMSE≤13.1%, good E≤0.76, high d≤0.94 and high R² values ≥0.98 and biomass development by low RMSE≤13.2%, high E≤0.92, good d≤0.68 and high R² values ≥0.95. During calibration, model well-simulated the CC under second sowing (D₂) and biomass under third sowing (D₃). Validation showed almost good fit of CC by low RMSE≤22.0%, good E≤0.68, high d≤0.84 and high R² values ≥0.97 and biomass development with low RMSE≤7.1%, good E≤0.66, good d≤0.60 and high R² values ≥0.98. During Validation, model well-simulated the CC and biomass under third sowing (D₃). Model showed good fit of yield during first sowing window (D₁) with a less deviation for both calibration and validation (15.6% and 5.8% respectively). From the result it could be concluded that sowing windows influence on baby corn production was accurately simulated using AquaCrop during calibration (R²=0.94) and validation (R²=0.98). Hence, AquaCrop proved to be a feasible tool for maximizing the Baby corn yield under different sowing windows.

Spatial estimation of water requirement in greengram under changing climates of North Interior Karnataka

2023-04-19T17:05:23+00:00

Greengram is one of the major protein rich grain legumes predominately cultivated in North Interior Karnataka (NIK). The study aimed at determining the water requirement of greengram variety DGGV- 2 using CROPWAT model that helps the farmers of NIK consisting of 12 districts in tapping the potential yields of this crop through proper irrigation management. The decadal analysis for 60 years was done under past (1991-2020) and projected climate (2021-2050) as per the recommended practices of UAS, Dharwad across four dates of sowing from 07^th June to 28^th June at weekly interval. The average crop evapotranspiration (ET_c), effective rainfall (ER) and irrigation requirement (IR) under past climates (1991-2020) for NIK were 246, 269.3 and 37.4 mm, respectively. An increase of 26.8 mm in ET_c, 21.6 mm in ER and decrease of 0.3 mm in IR were simulated under projected climates. Sowing late i.e., on 28^th June under projected climate (2021-2050) simulated the lowest water requirement and irrigation requirement for all the 12 districts of NIK. The spatial distribution of ET_c, ER and IR for all the 12 districts of NIK were interpreted under both past and projected climates using ArcGIS software.

Estimating irrigation water requirement in rice by integration of satellite data and agrometeorological indices in Palakkad, Kerala

2023-03-03T19:33:47+00:00

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 (K_c) 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 K_c values with an R²value of 0.81. Crop coefficient (K_c) maps developed using satellite derived NDVI provided K_c 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.

Techno economic feasibility of soybean based cropping systems under varying climates in Madhya Pradesh

2022-10-14T08:29:30+00:00

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.

Prediction of major pest incidence in Jute crop based on weather variables using statistical and machine learning models: A case study from West Bengal

2022-11-16T16:37:51+00:00

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.

Impact of temperature, moisture and CO2 on growth of pathogen and severity of emerging dry root rot disease of chickpea in Karnatak

2023-04-06T16:10:08+00:00

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.

Forecasting models for forewarning Anthracnose and web blight of mung bean (Vigna radiata) under Tarai zone of Uttarakhand

2022-12-14T20:05:25+00:00

Mung bean (Vigna radiata L.) is attacked by numerous diseases of which anthracnose and web blight are predominant in Tarai Zone of Uttarakhand. Anthracnose and web blight of mung bean are caused by Colletotrichum lindemuthianum and Rhizoctonia solani, respectively. Their occurrence and development are highly influenced by weather conditions during the cropping season. Based on epidemiological data recorded at Pantnagar for two consecutive years (2019 and 2020), disease predictive models were developed using stepwise multiple regression. The result from the recorded data revealed that rainfall, T_(min), rainy days, and morning relative humidity were statistically significant. Whereas, the T_(max), evening relative humidity, and bright sunshine hours were statistically non-significant. Significant weather parameters were employed to develop suitable web blight and anthracnose prediction models for commonly grown varieties of mung bean. The prediction models were further validated using the web blight and anthracnose incidence data collected in mung bean varieties in 2021. The root mean square error values varied between 0.0002 – 0.0011, which shows that the models are accurate and acceptable.

Impacts of climate change on future crop water demand in an agricultural watershed in Mayurbhanj district of Odisha, India

2023-02-01T21:16:58+00:00

Microclimate and thermal indices of garden pea (Pisum Sativum L.) under poplar (Populus Deltoides Bartr.) based agroforestry system

2023-02-24T16:37:40+00:00

Meta analysis on the evaluation and application of DSSAT in South Asia and China: Recent studies and the way forward

2023-01-15T15:06:27+00:00

The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R²), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R²~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R²~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R²~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R² of 0.99. The lowest R² and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R² as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices.

Decision support system for digitally climate informed services to farmers in India

2023-01-21T16:21:58+00:00

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.

Coordinated research on agrometeorology: India perspective

2023-02-13T09:43:51+00:00

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.

Climate variability, trends, projections and their impact on different crops: A case study of Gujarat, India

2023-03-03T20:16:10+00:00

Gujarat, being a coastal state, is likely to be impacted by global warming and climate change not only due to sea level rise and salinity ingress but also due to an increase in the frequency of cyclonic storms and other extreme weather events, causing uncertainty in crop production. An attempt has therefore been made to understand the climate of Gujarat in the past, present, and future based on the works done at the Department of Agricultural Meteorology, Anand. Analysis carried out on climatic trends and climatic extremes using past available data from different stations in Gujarat has been highlighted. Crop simulation models validated with experimental data collected for different crops across Gujarat state were used to understand the response of crops to climatic variability. The climate change impact studies and adaptation strategies carried out under the NPCC project have also been highlighted. And lastly, the work done by the author as an emeritus scientist on climate projections under RCP 4.5 and RCP 8.5 for all the districts of Gujarat and their likely impact on selected crops is presented. The results revealed that in the past, temperatures have shown increasing trends but not reached significant levels except at certain locations at night. Rainfall has also increased, but marginally. Future temperatures have been projected to increase in different parts of Gujarat under RCP4.5 and RCP8.5 with varying magnitudes. Similarly, the rainfall has also been projected to increase, while the sunshine hours are projected to decrease. The ultimate impact would be a drastic reduction in yields in spite of the increase in CO₂ level, suggesting that the present-day crop varieties would not be able to sustain crop production levels under a changing climatic scenario.