Crop Calendar Research Articles

How large is the farm income loss due to climate change? Evidence from India

PurposeClimate change is the most concerned issue in the global economy; increase in climate variability and uncertain climate events have caused distress in agriculture sector. The study estimates economic effect of climate change on agriculture income for the Indian state of Karnataka. The study reports the difference of result from past studies, where estimates from present study indicate higher negative impact of rise in temperature.Design/methodology/approachFixed effect panel regression method was used to examine change in agriculture revenue to climate response. Climate variables were classified based on the crop calendar to capture the damage caused by climate change. The authors use fine scale climate data set constructed at regional context for 20 districts and time period of 21 years (1992–2012).FindingsThe result showed that with 1-degree rise in average maximum temperature, the revenue declined by 17–21%. The prediction behavior of the different models was evaluated using out-of-sample forecast approach by training and testing historical data set.Originality/valueThe study adopts recent data sets on agriculture and the updated climate variables to estimate the climate change impact on agriculture. The study yields the better results when compared to previous traditional models applied in literature in Indian context. The study further evaluates the prediction behavior and robustness of the estimated models using out-of-sample forecast method.

Crop calendar optimization for climate change adaptation in rice-based multiple cropping systems of India and Bangladesh

Adjusting crop calendars may present an effective adaptation measure to avoid crop yield loss and reduce water use in a changing climate. In order to better understand potentials and limitations of adjusting crop calendars for climate change adaptation of tropical multi-cropping systems with short fallow periods, we used a regionally calibrated Environmental Policy Integrated Climate (EPIC) agronomic model to estimate annual caloric yield and blue water requirement (BWR) of irrigated double-rice and rice-wheat cropping systems in India and Bangladesh. We adjusted crop calendars by (a) single-objective optimization to maximize annual caloric yield and (b) multi-objective optimization to minimize BWR under current and future climate scenarios, focusing on climatic drivers of optimal growing seasons. While the short time intervals between harvest of kharif crops and (trans-)planting of rabi crops limit the space for planting date shift in the study area, our results indicate that crop calendar adjustment has great potential to reverse yield loss induced by temperature rise and decrease BWR by utilizing monsoon precipitation. The study indicates a trend towards earlier planting of rabi wheat to mitigate heat stress during the reproductive stage. Moreover, earlier planting of kharif rice can help to utilize monsoon precipitation, avoid cold stress of kharif rice during anthesis, and allow for early wheat sowing during the historic period. By the 2080s, the increase of heat stress in summer and the decrease of cold stress in winter seems to allow more flexibility for late rice in kharif season, but a conflict between later planting for yield improvement and earlier planting for blue water saving is expected in kharif rice on the Indo-Gangetic plain of India and Bangladesh. Therefore, the trade-off between yield improvement and irrigation water use needs to be carefully considered to promote adaptive adjustment of crop calendars under climate change.

A perspective for advancing climate prediction services in Brazil

AbstractThe Climate Science for Service Partnership Brazil (CSSP‐Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP‐Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research‐to‐services (R2S) and a services‐to‐research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy‐relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower.

OBSERVATIONS, PERCEPTIONS ET ADAPTATIONS AU CHANGEMENT CLIMATIQUE DANS LA SOUS-PRÉFECTURE DE SÉGUÉLA (CÔTE D’IVOIRE)

In a context of sustainable development and food security, the fight against climate change has become a priority. In tropical rural areas, its effects are beginning to appear by affecting agricultural production, which is a factor that aggravates socio-economic impacts. Indeed, understanding the determinisms of climate change as well as their perception and the adaptation strategies developed by rural populations is an issue of food security and socio-economic development. In the sub-prefecture of Séguéla, the perceptions and adaptations to climate change of populations living from rain-fed agriculture are acute. This article analyzes on the one hand the climatic variability of this sub-prefecture based on scientific observations of the annual accumulations of precipitation from 1986 to 2015. And on the other hand, following a questionnaire survey, the perception and representation of this climatic variability as well as the strategies developed in response by farmers were noted. The main results indicate a decrease in rainfall in general modifying the crop calendar of farmers. In addition, the study attests that peasant perceptions and representations are linked to their local beliefs. This then results in dynamics and modalities of adaptation through the introduction of new cultures / associations of cultures, the development of lowlands reflecting the interactions of the peasant population with its environment.

An assessment of water footprint for irrigated rice in Punjab


 Water scarcity has become one of the highest risks for environmental and economic security worldwide. The water footprint for rice production, i.e., the total volume of freshwater consumed to produce a unit quantity rice has been estimated for three different agroclimatic zones of Punjab for the years 2000 to 2017. The results revealed that effective rainfall (Peff) improved in later years due to a change in crop calendar,legally enforced by an Act prohibiting the transplanting of paddy before a specified date. During the period of study, minimum crop evapotranspiration (ETc) was 4645 and maximum was ETc of 9511 m3ha-1 during 2014 and 2011, respectively. The green water footprint (WFgreen) for rice varied from 646litreskg-1of rice during low rainfall years (2012) to 1149 litre kg-1 of rice during heavy rainfall (1192 mm) during 2011.Out of a total water footprint (WFtotal) of 2650 litre kg-1, the share of blue water footprint (WFblue) was higher 1804 litre kg-1 (68%), indicating a need to improve on-farm irrigation management to conserve water resources.

The Role of Sequential Cropping and Biogasdoneright™ in Enhancing the Sustainability of Agricultural Systems in Europe

Sequential cropping in the Biogasdoneright™ (BDR™) system in Italy has recently gained attention to combine food and renewable energy production in a sustainable way, as well as for carbon sequestration. However, little is known on the potential to expand the practice in other regions of Europe. In this paper, sequential crop calendars were developed for different EU climate regions, and the EU biomethane potential of the anaerobic digestion (AD) of sequential crops was estimated for a Conservative_Scenario and a Maximum_Scenario, assuming different percentages of primary crop land dedicated to the practice and biogas yields. A total EU biomethane potential of 46 bcm/yr and 185 bcm/yr was estimated from the AD of sequential crops in the two scenarios, respectively, and the Continental region registered the highest potential compared to the other regions. The additional benefits of the combination of sequential cropping with other agricultural conservation practices and digestate use included in BDR™ systems were also discussed. In conclusion, the paper shows that with appropriate innovations in crop management, sequential cropping could be applied in different agroclimatic regions of Europe, contributing to climate and renewable energy targets.

CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt

ABSTRACT The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity exports and is changing its landscape. Hence, efforts to accurate land use and land cover (LULC) monitoring in this region are strategic, due to its role in Brazil’s food, environmental, and economic security policy. Thinking on planning and technical sovereignty in the spatial sector, the China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide useful data for decision-makers to manage the Brazilian territory independently of external policies. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are largely applied in deforestation monitoring by remote sensing specialists but less applied than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, data cubes enabled storing and accessing large spatio-temporal analysis-ready data. Within this scope, the Brazil Data Cube Project (BDC) creates multidimensional data cubes from orbital sensors’ data for all Brazilian territory. We applied BDC CBERS-4/WFI data cubes to generate LULC classifications for the Extremo Oeste Baiano agricultural belt correspondent to the 2017/2018 and 2019/2020 harvest periods, at two levels of detail: broad and crop type, incorporating ground truth samples, crop calendar knowledge, and vegetation indices to a dense time series analysis approach. Overall Accuracies were equal to 0.87 and 0.89 for broad, and 0.91 and 0.94 for crop type classifications. The results indicate CBERS-4/WFI data cubes as a useful tool for improving crop monitoring in the Cerrado biome based on machine learning.

RICA: A rice crop calendar for Asia based on MODIS multi year data

Information on when and where rice is planted and harvested is important for crop management under a changing climate and for monitoring crop production for early warning and market information systems. The diversity of plant genetic, crop management, and environmental conditions leads to a wide variation in the number of rice crops per year and the dates of crop establishment and harvesting across Asia. Asia-wide rice crop calendars exist (e.g., RiceAtlas) but are based on heterogeneous data sources with varying levels of detail and are challenging to update. Earth observations can contribute to consistent and replicable crop calendars. Here we demonstrate and validate a method for generating a rice crop calendar across Asia. Our analysis at administrative unit-level is based on pixel-level analysis with the PhenoRice algorithm using MODIS imagery (2003–16) to estimate start of season (SoS) and end of season (EoS) dates. PhenoRice outputs were post-processed to generate representative statistics on the number of rice crop seasons per year and their SoS/EoS dates per administrative unit across Asia, called RICA (a RIce crop Calendar for Asia). RICA SoS and EoS dates across all seasons correlated strongly with RiceAtlas crop establishment and harvesting dates (R2 of 0.88 and 0.82 respectively, n = 1,186). The mean absolute errors were around 26 and 33 days for SoS and EoS, respectively. A detailed assessment in the Philippines where data in RiceAtlas are particularly accurate had even better results (R2 of 0.93 and 0.85 respectively, n = 131). Comparisons to other published rice calendars also suggested that RICA captured rice cropping season dates well. Our study results in a unique and validated method to estimate rice crop calendar information on continental scale from remote sensing data.

Effective Farm Business Strategy for Local Commodities in Haiti; Case of Maize

Low productivity seems to be the biggest issue faced by the Haitian farming. Besides, previous studies concluded that this problem is due specifically to inadequate or poor quality of input used by the farmers. The economic condition of the farmers prevents them from even meeting the crop calendar. This study suggests that boosting the agricultural investment by the mean of contract farming to promote the access to an improved package of technologies, would be an effective strategy to overcome the current situation. The results show that the adequacy of the package of technologies has a significant influence on the productivity. Furthermore, as a new phenomenon for Haiti, the contract farming benefits a high level of willingness from the Haitian farmers. In addition, the farmers with low annual income and/or greater area cultivated seem to be more likely to accept the contract farming.

Transformational Adaptation in Agriculture under Climate Change: A Case Study in the Dry Zone of Sri Lanka

Transformational adaptation defines as 'changes the fundamental attributes of a system in response to climate and its effects.' Farmers deal with the natural environment and its components such as rainfall, temperature, humidity, and soil condition, which have a high range of variability and uncertainty for their cultivation. The present study focused on the impacts of climate change on the settler community who engage in agriculture as their mainstay and respond to the scenario. Quantitative and qualitative methods have been applied. Twenty samples from a village in the NCP have been selected. Primary outcomes of this study are (a) total awareness of perceptions on climate change; (b) the ambient temperature has been increasing and resulting in more heat stress; (c) frequent and severe occurrence of extreme rainfall anomalies and increasing trend of natural calamities. The area farmers have been adopting several strategies to overcome the negative impacts of climate change, such as transforming from intensification to more intensification that can be identified as Climate Smart Agriculture; crop diversification and adaptation of drought tolerance crops; transforming from agriculture to animal husbandry, and out-migration of unemployed or evicted youth from agriculture to non-agriculture. Institutional involvement is essential to strengthening the adaptative strategies of the people by providing an appropriate crop calendar and suitable crop combination and aware of the way of improving the use of the efficiency of available water for improving the living standard of the people.

Impact of anthropogenic activities on river-aquifer exchange flux in an irrigation dominated Ganga river sub-basin

The easy accessibility of groundwater in the alluvial plains of the Kosi river basin has considerably altered the crop calendar and farmers’ irrigation response, especially in the dry years after 2004. The present study attempts to develop a framework for analyzing the spatio-temporal variation in the groundwater recharge/ extraction pattern as well as river and aquifer flux exchange due to increasing anthropogenic activities in the basin. The influence of anthropogenic activities over groundwater recharge/extraction statistics has been discussed in Laveti et al. (2021). The estimated groundwater recharge (Laveti et al., 2021) along with aquifer characteristics (hydraulic conductivity, porosity, and specific storage), and riverbed conductance were used as inputs to the groundwater flow transient model in the study. The model observed, an increasing groundwater dependency for agricultural activities, results in a continuous increase of flux from the river to the aquifer. Between 1997 and 2010, the river to aquifer flux increased by almost 1% of the average river discharge. This increase in river to aquifer flux exchange was evident, especially during the dry periods after 2004, which coincided with the government’s Million Shallow Tube Well Programme (MSTP) in the basin. The increasing trend of the river to aquifer flux transfer and the decreasing trend of the aquifer to river flux transfer observed in the Kosi basin can have disturbing long-term effects in terms of reduced river flow and river stage. Therefore, the concerned authorities and the stakeholders must take immediate notice of the observations.

Irrigated areas drive irrigation water withdrawals

A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty. Modelling irrigation water withdrawals as a function of irrigated areas yields almost the same results in a much parsimonious way, while permitting the exploration of all model uncertainties. Our work offers a robust and more transparent approach to estimate one of the most important indicators guiding our policies on water security worldwide.

Existences and perception of javanese local knowledge for climate change adaptation

Farmer perception of their environment is a factor of climate change. Adaptation to climate change requires farmers to realize that the climate has been changed and they must identify useful adaptations and implement them. This study analyzed the adaptation of climate change among rural farmers using local knowledge and knowing the existence of it. Local knowledge was examined by descriptive qualitative. The results showed that Pranata Mangsa crop calendar was used by Javanese society for a long time ago, where, practiced by the indigenous people through their different beliefs, culture, and traditions. Pranata Mangsa comes from two words, Pranata which means rule and Mangsa which means season or time, where, divided into four (4) seasons: Katiga (dry season), Labuh (dry to rainy season), Rendheng (rainy season), and Mareng (rainy to dry season), which each season has natural signs for warning system and move to next season. In addition, the results showed that the Javanese societies said Pranata Mangsa is still relevant (31.71%), need to re-evaluate (22%), not relevant (13.3%), and not answered (33%).

Farmers’ adaptation in dealing with limited water (A case study on Wonogiri Regency)

The vulnerability of farmers to drought is determined by the interaction of the potential impacts of climate change. The impact of climate change is felt by residents of Paranggupito District, one of which is in Ketos Village in the agricultural sector, especially rainfed land which is very vulnerable to long drought conditions. Farmers ‘losses to drought are determined by the interaction of climate change impacts and farmers’ adaptation. This research aims to find out the adapting strategy the farmers take in dealing with water limitation. The subject of research consisted of farmers in Ketos Village, Paranggupito Sub District, Wonogiri Regency. The research design used was descriptive qualitative one with purposive and snowball sampling techniques. Data collection was carried out using observation, in-depth interview, focus group discussion, and documentation. Data was analyzed using triangulation technique. The result of research shows that to adapt to their condition, the farmers adopt short-and medium-term adapting behaviour, including: determining crop calendar, selecting plant variety, applying intercropping system, determining appropriate planting pattern, controlling pest and disease, determining planting technique, and monitoring continuously.

Will Climate Change Exacerbate the Economic Damage of Flood to Agricultural Production? A Case Study of Rice in Ha Tinh Province, Vietnam

In developing countries in general and in Vietnam in particular, flood induced economic loss of agriculture is a serious concern since the livelihood of large populations depends on agricultural production. The objective of this study was to examine if climate change would exacerbate flood damage to agricultural production with a case study of rice production in Huong Son District of Ha Tinh Province, North-central Vietnam. The study applied a modeling approach for the prediction. Extreme precipitation and its return periods were calculated by the Generalized Extreme Value distribution method using historical daily observations and output of the MRI-CGCM3 climate model. The projected extreme precipitation data was then employed as an input of the Mike Flood model for flood modeling. Finally, an integrated approach employing flood depth and duration and crop calendar was used for the prediction of potential economic loss of rice production. Results of the study show that in comparison with the baseline period, an increase of 49.14% in the intensity of extreme precipitation was expected, while the frequency would increase 5 times by 2050s. As a result, the seriousness of floods would increase under climate change impacts as they would become more intensified, deeper and longer, and consequently the economic loss of rice production would increase significantly. While the level of peak flow was projected to rise nearly 1 m, leading the area of rice inundated to increase by 12.61%, the value of damage would rise by over 21% by 2050s compared to the baseline period. The findings of the present study are useful for long-term agricultural and infrastructural planning in order to tackle potential flooding threats to agricultural production under climate change impacts.

Food Security Under The Era Of Climate Change Threat

Food Security Under The Era Of Climate Change Threat

Spatiotemporal variability and time series trends of rainfall over northwestern parts of Ethiopia: the case of Horro Guduru Wollega Zone.

This study examined the variability and trend of rainfall over Horro Guduru Wollega Zone. Studies such as this have paramount importance in countries and areas where rain-fed agriculture is predominant. Datasets for analysis were obtained from the National Meteorological Agency of Ethiopia (NMA) from 1987 to 2016 and Climate Hazard Group InfraRed Precipitation (CHIRPS) 1987-2019 with the station portal. Monthly rainfall with temporal variability ranging from 9.77 to 141.93% was observed. High variable (CV > 30%) and less variable (CV < 20%) rainfall was observed in the CHIRPS data records. Rainfall during most months of the last 30 and 33years showed a decreasing trend. Rainfalls with temporal variability ranging from 12.7 to 75.92% and from 8.11 to 43.45% were observed during the 3-month seasons, respectively. Rainfall over the 3-month seasons of the last 30 and 33years showed a decreasing trend. The average total rainfall ranging from 107.203 to 1016.82mm and from 122.8 to 1147.9mm, with variability from 9.163 to 55.7% and from 7.831 to 36.68% were observed during the Belg, Kiremt, and Bega seasons of the last 30 and 33years, respectively. A decrease in rainfall was tested over these three seasons of the last 30 and 33years. Significantly different (P < 0.05) and less variable (CV < 20%) annual total rainfall was recorded at 24 stations over 30years. Declining annual rainfall was observed over 30 and 33years. Non-significantly different (P < 0.05) and less variable (CV < 20%) average decadal rainfall ranging from 1342.6 to 1372.8mm was observed during the last 33years. The study area had experienced a rainfall with decreasing trends almost over all time scales. These might have been resulting in failure of agricultural production that necessitates developing and implementing systematic planning and management activities in the crop calendar under the face of changing rainfall patterns.

Maize and sorghum field segregation using multi-temporal Sentinel-2 data in central Mexico

Crop segregation in optical remote sensing cannot always be easily achieved among similar species since their spectral response can be similar. We aimed to distinguish fields of two C4 gramineae with similar plant structure and crop calendar, maize (Zea mays) and sorghum (Sorghum bicolor), in an agricultural area of the state of Guanajuato, in central México. One hundred and forty-three fields of these two crops were identified and their average spectral signatures were derived at different phenological stages, using digital information from 11 Sentinel 2A/B images, corresponding to the 2019 spring–summer cycle. We also calculated the temporal profile of six spectral indices for each crop, to evaluate seasonal variations and conducted a cropland classification, using the random forests algorithm and different combinations of dates and indices. Results show that segregation of maize and sorghum fields can be achieved due to small differences in the near-infrared region of their spectral signatures and disparities in the rate of decay during the senescence stage. Classification results also confirmed the separability of these two crops, obtaining a very high overall accuracy and kappa values (98.43% and 97.07%, correspondingly) based on the reflectance of nine images and only three spectral indices: the normalized difference vegetation index, the normalized difference water index, and the structure insensitive pigment index. This investigation demonstrates that the combined use of time series of high-resolution images and spectral indices can contribute to distinguish fields of similar crop species.

Crop Yield Estimation at Gram Panchayat Scale by Integrating Field, Weather and Satellite Data with Crop Simulation Models

Advanced technologies can improve the operational implementation of the Indian national crop insurance scheme, the Pradhan Mantri Fasal Bima Yojana (PMFBY), particularly in terms of accuracy and timeliness of the crop yield estimates that are used to determine yield losses at the Gram Panchayat (GP) level. In this study, conducted as a pilot test for PMFBY during the kharif season of 2018, technologies based on the Terrestrial Observation and Prediction System (TOPS) were tested and implemented for estimating GP-level crop yields of bajra (pearl millet, Pennisetum glaucum) in Firozabad District of Uttar Pradesh and rice (Oryza sativa) in Kendujhar District of Odisha. A combination of Synthetic Aperture Radar and optical data was used to map crop extent. Daily 2-km grids of input weather conditions were generated using a machine learning algorithm that incorporated station observations, satellite data, and reanalysis model outputs. Required crop biophysical estimates of leaf area index (LAI) and the fraction of intercepted photosynthetically active radiation (FPAR) were derived using daily cloud-screened MODIS 250-m data from the Terra and Aqua satellites and a modified MOD15 LAI/FPAR backup algorithm. A light-use-efficiency (LUE) model adapted from the MODIS (Moderate Resolution Imaging Spectroradiometer) algorithm (MOD17-GPP/NPP) was then used to spatially estimate crop yields. Crop extent maps, daily climate and gap-filled FPAR and the LUE model were used to estimate above-ground biomass, which was accumulated over the growing season and converted to crop yields using a crop-specific harvest index. The estimated yields at 250 m were aggregated within each GP and compared with crop yield data from crop cutting experiments (CCEs) conducted in 142 GPs for rice and 42 GPs for bajra. Crop extent mapping was 96% accurate in rice and 80% in bajra when validated with field surveys. A comparison of modeled yields with CCE yields showed a promising performance by the model in both crops (rice: r = 0.80, root-mean-square error (RMSE) = 411 kg/ha, mean absolute error (MAE) = 359 kg/ha, percent error (PE) = 7, Observed mean = 1500 kg/ha; Bajra: r = 0.84, RMSE = 309 kg/ha, MAE = 262 kg/ha, PE = −12.8, Observed mean = 1859 kg/ha). Although the approach showed promising results for both crops, further progress is needed to ensure consistent and reliable results. Some of the needed improvements include incorporating a dynamic crop calendar, improved maximum LUE estimates, and harvest index values that represent crop varietals grown in India. Routinely conducted CCEs in different crops and seasons around the country could provide a valuable resource for improving these parameters and, ultimately, crop yield estimates.

Important pest species of the Spodoptera complex: Biology, thermal requirements and ecological zoning

In South America, especially in Brazil, four members of the Spodoptera complex, Spodoptera albula (Walker, 1857), S. cosmioides (Walker, 1858), S. eridania (Stoll, 1782), and S. frugiperda (J.E. Smith, 1797) are important pests of many crops, in particular corn, soybean and cotton crops. Spodoptera eridania and S. frugiperda have recently invaded Africa and caused serious crop damage, and S. frugiperda has invaded Asia and Oceania. The present study tested the effect of a range of seven temperatures (18–34 °C) on these four Spodoptera species simultaneously, assessing several biological variables. Based on the thermal tolerances obtained experimentally, the ecological zoning of each species in Brazil was mapped and compared spatially, according to the crop calendar of three important crops in different regions (first and second corn harvest, soybean and cotton). Our results showed that S. eridania had the lowest temperature threshold (Tt), i.e., it is favored in regions with more moderate temperatures; and did not tolerate the warmest temperature, failing to complete its development at 34 °C. In contrast, S. albula did not complete its development at 18 °C and may be more successful in warmer regions. In general, S. frugiperda and S. cosmioides were able to develop over a wide range of temperatures, and S. frugiperda showed a higher biological potential at all temperatures evaluated. Our biological data and the computational code are available online. The extensive data produced here can help other entomologists to delimit the spatial distribution of the Spodoptera complex and forecast outbreaks of these pests.