Crop Calendar Research Articles

Global crop yields can be lifted by timely adaptation of growing periods to climate change

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments.

Comparative evaluation of NDVI strata and livelihood zones as spatial units of the practiced crop calendars

ABSTRACT Properly mapping the spatial variability of the practiced crop calendars is useful for precise planning of agricultural activities. A seasonal calendar based on the livelihood zone (LHZ) map is widely employed as a crop calendar in Ethiopia. However, using the LHZ-based crop calendar for agricultural purposes may cause problems of doubtful accuracy, since non-ecological parameters are included in the LHZ map. This research was conducted in the central and southeastern parts of Ethiopia to compare the hypertemporal normalized difference vegetation index (NDVI) and LHZ-based stratification methods in explaining the spatial variability of the farmers’ actual crop calendar. A NDVI strata generated from SPOT-VGT and PROBA-V NDVI data of 16 years and the 2008 LHZ map of Ethiopia were processed to select sampling sites that represented three NDVI strata and four LHZs in common. Interview data were collected randomly from individual farmers on ploughing, planting and expected harvesting dates of teff (Eragrostis tef), wheat and barley (fields), and were analysed using non-parametric tests. The results revealed that the NDVI strata had greater spatial quality and precision than the LHZs in terms of uniqueness and internal homogeneity of the spatial units, indicating its suitability as a preferable alternative crop calendar.

Assessing the impact of Sentinel-1 derived planting dates on rice crop yield modeling

• Sentinel-1A SAR-based dynamic planting dates (PD) for farm management activities. • Difference between SAR-based PD and the Crop Calendar based PD can be <=75 days. • SAR-based PD significantly improves rice yield estimates using crop models. Planting date (PD) is one of the critical component in crop modeling that has a large-scale impact on seasonal yield estimation. Here, we aimed to estimate the various paddy/rice attributes (particularly, acreage and PD) using the Sentinel-1A observations, and assessing the impact of such key parameters on end-of-season yields. The method comprises: (1) a multi-temporal time-series analysis algorithm of Sentinel-1A observations for rice mapping, (2) a PD retrieval algorithm, and (3) rice yield estimation using the Synthetic Aperture Radar (SAR)-derived PD in a modeling system. Our study was implemented over the rainfed paddy fields in Cambodia between 2017 thru 2020. We demonstrated that the backscatter ratio ( σ VH 0 σ VV 0 ) from Sentinel-1A can improve the accuracy of rice mapping, while σ VH 0 backscatter can provide PD estimations. Our results showcased reasonable/good performance in mapping the paddy fields and estimating PD in the study area. PD found in March and April were indicative of irrigation application in some parts of Cambodia, specifically in the southeastern part (also part of the Mekong delta) where irrigation in paddy fields is a common practice. Differences between the crop-calendar based PD (June 1) and the SAR-derived PD can range as much as 75 days. In the rice-dominated provinces such as Banteay-Meanchey and Svay-Rieng of Cambodia, when SAR-derived PD are used, rice yield normalized bias improved compared to the fixed PD data by a mean of 7–12 % and 30–48 % respectively within the study period. In addition, the PD derived from SAR also significantly reduced the uncertainty in yield estimation. Overall, this study demonstrated the potential of using the SAR-derived PD in improving the rice yield estimation over rainfed paddy fields.

Characterizing country-specific human and ecosystem health impact and damage cost of agricultural pesticides: the case for Thailand

PurposeExisting emission and toxicity characterization models in life cycle assessment are currently not suitable for assessing pesticide-related impacts of crop cultivation in tropical regions. This study aims to parameterize the scientific consensus model USEtox for Thai environments to derive toxicity characterization factors of pesticide emissions from agricultural systems in Thailand. Potential human toxicity and ecotoxicity impacts and related damage costs of pesticides used on nine crops cultivated in Thailand are quantified.MethodsConsidering country-specific conditions, USEtox was adapted by applying the landscape and consumption parameters specific to Thailand. Related Thai-specific characterization factors of identified pesticides used in Thai agriculture were quantified. Four emission inventory models were applied to determine pesticide emission fractions in different environmental compartments. The consistent combination of pesticide emission mass and associated characterization factors yielded potential toxicity impact scores. Pesticide impact-related damage costs (external costs) on human health and ecosystem quality were quantified using valuation factors for Thailand. The crops with the highest total damage costs were selected and compared with the annual net incomes of the respective crop production systems.Results and discussionPesticide toxicity impacts assessed by using Thai-specific factors were different from the use of global average factors ranging from 1 to 169% (human toxicity) and from 0.1 to 3587% (ecotoxicity). Our results indicated the variability in impact scores influenced by emission modeling choices. Following PestLCI consensus emission estimation model, mango cultivation showed the highest human toxicity impacts of 0.07 DALY/ha, resulting in high human health damage costs mainly caused by Propineb (93%). Rice cultivation with a dry direct-seeded system exhibited the highest ecotoxicity impacts (3934 PDF m3 day/ha) and associated damage costs mainly caused by Oxadiazon (92%). Pesticides used in cultivation of nine crops resulted in total damage costs of 7188 and 3.01 million THB/crop-year for human health and ecotoxicity, respectively. Mango and rice production accounted for 70% and 17% of the total damage costs, which were 36% and 20% of the respective crops’ annual net income.ConclusionsOur study illustrates the ranking of pesticides applied throughout the crop calendar causing toxicological impact and related damage costs on human health and ecosystem quality. This helps identify crops and the main contributors to pesticide-related toxicity impacts in Thailand. Our study highlights the need for proper emission quantification and for the use of characterization factors locally parameterized to increase accuracy. Our results will be useful for future improvement toward more sustainable pesticide use.Graphical abstract

Investigating Sentinel-1 and Sentinel-2 Data Efficiency in Studying the Temporal Behavior of Wheat Phenological Stages Using Google Earth Engine

Crop monitoring is critical for sustaining agriculture, preserving natural resources, and dealing with the effects of population growth and climate change. The Sentinel missions, Sentinel-1 and Sentinel-2, provide open imagery at a high spatial and temporal resolution. This research aimed (1) to evaluate the temporal profiles derived from Sentinel-1 and Sentinel-2 time series data in deducing the dates of the phenological stages of wheat from germination to the fully mature plant using the Google Earth Engine (GEE) JavaScript interface and (2) to assess the relationship between phenological stages and optical/ SAR remote sensing indices for developing an accurate phenology estimation model of wheat and extrapolate it to the regional scale. Firstly, the temporal profiles derived from Sentinel-1 and Sentinel-2 remote sensing indices were evaluated in terms of deducing the dates of the phenological stages of wheat. Secondly, the remote sensing indices were used to assess their relationship with phenological stages using the linear regression (LR) technique. Thirdly, the best performing optical and radar remote sensing indices were selected for phenological stage prediction. Fourthly, the spatial distribution of wheat in the TIP region was mapped by performing a Random Forest (RF) classification of the fusion of Sentinel-1 and Sentinel 2 images, with an overall accuracy of 95.02%. These results were used to characterize the growth of wheat on the TIP regional scale using the Temporal Normalized Phenology Index (TNPI) and the predicted models. The obtained results revealed that (1) the temporal profiles of the dense time series of Sentinel-1 and Sentinel-2 indices allowed the dates of the germination, tillering, jointing heading, maturity, and harvesting stages to be determined with the support of the crop calendar. (2) The TNPIincrease and TNPIdecrease revealed that the declining part of the NDVI profile from NDVIMax, to NDVIMin2 revealed higher TNPI values (from 0.58 to 1) than the rising part (from 0.08 to 0.58). (3) The most accurate models for predicting phenological stages were generated from the WDVI and VH–VV remote sensing indices, having an R2 equal to 0.70 from germination to jointing and an R2 equal to 0.84 from heading to maturity.

Monitoring Growth Status of Winter Oilseed Rape by NDVI and NDYI Derived from UAV-Based Red–Green–Blue Imagery

The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands’ wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages.

Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers

Detecting crop calendar changes is critically important for crop monitoring and management, but the lack of annual, Asia-wide, and long-term rice calendar datasets limits our understanding of rice phenological changes and their climate drivers. In this study, we retrieved key rice phenological dates from the GLASS AVHRR LAI through combining threshold-based and inflection-based detection methods, analyzed the changes during the period 1995–2015, and identified the key climate drivers of the main rice seasons in Asia. The retrieved phenological dates had a high level of agreement with the referenced observations. All R2 were greater than 0.80. The length of the vegetation growing period (VGP) was mostly shortened (by an average of −4 days per decade), while the length of the reproductive growing period was mostly prolonged (by an average of 2 days per decade). Moreover, solar radiation had the most significant impact on the rice calendar changes, followed by the maximum and minimum temperatures. The VGP in tropical areas is the most sensitive to climate change. Our study extends the annual rice phenology dynamics to a higher spatial–temporal resolution and provides new insights into rice calendar changes and their climate drivers, which will assist governments and researchers regarding food security and agricultural sustainability.

Modelling climate change impacts on wet and dry season rice in Cambodia

AbstractIrregular rainfall, rising temperatures and changing frequency and intensity of extreme weather events are projected to reduce crop yields and threaten food security across the tropical monsoon sub‐region. However, the anticipated extent of impact on crop yields and crop water productivity (CWP) is not yet thoroughly understood. The impacts of climate change on rice yields and CWP are assessed over the Northern Tonle Sap Basin in Cambodia by applying the AquaCrop model into the mid‐ (2041–2070) to long‐future (2071–2099) under two Representative Concentration Pathways (RCPs) (4.5 and 8.5). Short (95 days), medium (125 days) and long (155 days) cycle varieties are tested during the wet and dry seasons. An assessment of different sowing dates and irrigation strategies (fixed and net irrigation during the dry season) elucidated the variation in response to changing environmental conditions. Higher yields (+15% by 2041–2070 and +30% by 2071–2099) and CWP values (+42% by 2071–2099) are expected if using short‐cycle varieties, in particular when sown in July. Dry season rice yields are also projected to increase (+28% by 2071–2099), especially under a higher greenhouse gas emission scenario (RCP 8.5) compared to a medium emission scenario (RCP 4.5) as a result of the CO2 fertilization effect. Depending on the climatic scenario, rice variety, irrigation scheme, and sowing date, increasing heat and drought‐stress conditions are likely to have different impacts on rice yields and CWP over time. Overall, this study highlights the benefits of adjusting crop calendars to identify the most suitable irrigation schedules and rice varieties to effectively adapt to projected future climate.

Land suitability assessment for second cropping in terms of low temperature stresses using landsat TIRS sensor

The second cropping which is done in the autumn after the main cultivation is one of the ways to increase food security, farmers' income, and thus prevent migration of villagers. The main limiting factor in autumn cultivation is the reduction in ambient and soil temperatures, which causes a severe drop in crop yield if it reaches below the physiological base temperature and freezing temperature. In Gilan, one of the northern provinces of Iran more than 90 % of agricultural land is cultivated once a year and has no production in half of the year. Accordingly, the purpose of the present study is to evaluate the feasibility of autumn cultivation of rice and hull-less seed pumpkin (Cucurbita pepo L. var. pepo subsp. styriaca) in terms low temperature stresses. In the first step, the land use map was prepared using the fusion of images with panchromatic band of Landsat 8 Maximum Likelihood classification method and then agricultural lands were separated. The land surface temperature (LST) maps were extracted on two dates, October 30, 2018 and December 17, 2019 using the TIRS sensor. Comparison of the estimated data with the meteorological data showed that LST was 0.6 °C higher than the meteorological data on October and it was 1.1 °C lower on December. Then, the LST map was validated with meteorological data in the last 10 years and a modified map was obtained to measure the cultivation potential of rice and hull-less seed pumpkin. Comparison of LST maps obtained on the two dates showed that the temperature variation was greater in the colder date. Considering base temperatures of 12 and 10 °C for rice and pumpkin, respectively, as well as the freezing temperature of 5 °C for these two crops, land suitability was identified. Having the minimum temperature of 15 °C for almost all lands, there will be no temperature stress for the crops until October 30. But, if the crop calendar lasts until December 17, only 2.62 % of agricultural lands have the potential to cultivate rice without temperature restrictions. Meanwhile, lands with cultivation potential for pumpkin were estimated 6983.3 ha (83.5 %) on December. Therefore, pumpkin cultivation has a lower temperature risk compared to rice in Rezvanshahr. The results of this study, in addition to being the required source for any planning in the field of autumn cultivation of rice and pumpkin, is one of the main sources for selecting different crops of the second cropping and their crop calendar.

Spatial and Temporal Evolution of Sowing and the Onset of the Rainy Season in a Region of Large Agricultural Expansion in Brazil

In order to assist in high-yield agricultural management in multiple cropping systems, it is essential to understand the link between the rainy season onset and crops sowing dates, since it considerably affects the management, yield and output. We built crop calendars derived from remote sensing products and investigated the link between sowing dates and the onset of the rainy season in irrigated and rainfed agriculture in Western Bahia, a new and important agricultural frontier in Brazilian Cerrado. Crop sowing dates were obtained from green-up dates from 2001 to 2019. Rainy season onset dates were determined using CHIRPS daily precipitation data. Results indicate that sowing occurs from 26 October to 15 November and the rainy season starts from 17 to 27 October. Rainfed sowing dates are strongly correlated to rainy season onset and are particularly affected in years where rains are delayed. Sowing dates in irrigated pixels occur up to 25 days earlier than rainfed and are not correlated to rainy season onset. Irrigated farms are sowing earlier and in a shorter window than rainfed, with a stronger resilience in years where rains are delayed, and have adapted their sowing operation towards a more intensive agriculture and efficient water use during the rainy season.

Modeling fall armyworm resistance in Bt-maize areas during crop and off-seasons

Entomologists have often used computational modeling to study the dynamics of insects in agricultural landscapes. Recently, important issues such as the movement of adults and immatures associated with insect resistance to GMO (genetically modified organism) crops have been addressed using computational models. We developed an individual-based model using the cellular automata approach (CA) to investigate how an intercropping system composed of maize engineered with Bacillus thuringiensis (Bt) gene, refuge areas (non-Bt maize), and grasses combined with off-season periods might influence the evolution of resistance in Spodoptera frugiperda (Lepidoptera: Noctuidae), one of the leading agricultural pests targeted by GMOs. We designed the Bt and non-Bt plants in two different arrangements: (a) a seed mixture and (b) strips rows, adding grasses in areas adjacent to the field. We added the seasonal planting dynamics (crop season and off-season), to evaluate a total of six agricultural scenarios. We followed a crop calendar from the United States to create simulations close to agricultural practice. The results showed that the frequency of the resistance allele was strongly related to the landscape arrangements and their dynamics. Since the adult insects are mobile, the seed-mixture scenario increased the frequency of the resistance the most (95.86%), followed by strips (82.10%), without grass fields. The maize harvest made it possible to reduce the frequency of resistance allele below 1%. Based on our results, we can expect that the maintenance of pasture areas, for instance next to the corn crops, will act as a reservoir of susceptible insects during off-season periods.

Mapping and Comparison of Maize Products Value Chains in Nigeria and Rwanda

Maize products are very significant for domestic consumption as well as industrial uses both locally and globally. For there to truly appreciate the spread of maize production in Africa, the geospatial mapping and subsequent comparison of the value chain for Nigeria and Rwanda were necessitated hence the purpose of this study. Farm mapping geospatial techniques and remotely sensed data were used for both Nigeria and Rwanda in this study. GIMMS Global Agricultural Monitoring data from United States Department of Agriculture (USDA) were adopted for Nigeria and Rwanda. The crop calendars of both countries were examined which thereafter reviewed a marked distinction among them. The results of the agroecological zones for the two countries showed a significant variation in their distribution and types, which in turn affect both the planting and harvesting of maize; storage, marketing, processing, and policy framework for maize products value chain in Nigeria and Rwanda. Mapping of the two countries was carried out and the normalized differential vegetation index (NDVI) and the policy associated with maize value chains were checked and reported.

Global crop calendars of maize and wheat in the framework of the WorldCereal project

ABSTRACT Crop calendars provide valuable information on the timing of important stages of crop development such as the planting or Start of Season (SOS) and harvesting dates or End of Season (EOS). This information is critical for many crop monitoring applications such as crop-type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop calendars provides an important asset in this respect, as it allows the algorithms to account for specific local circumstances while also maximizing their robustness and global applicability. Existing global crop calendar products, as produced by the Group on Earth Observations’ Global Agricultural Monitoring (GEOGLAM) Crop Monitor, the United States Department of Agriculture Foreign Agricultural Service (USDA-FAS), the Food and Agriculture Organization (FAO), and the European Commission Joint Research Center’s Anomaly hot Spots of Agricultural Production (ASAP), generally provide this information only at national or subnational level. In this work, we present gridded SOS and EOS maps for wheat and maize that represent the crop calendars’ spatial variability at 0.5° spatial resolution. These maps are generated in the framework of WorldCereal, which is a European Space Agency (ESA) funded project whose cropland and crop-type wheat and maize algorithms at global scale and at 10 m spatial resolution require this information. The proposed maps are built leveraging the above noted global products (Crop Monitor, USDA-FAS, FAO, ASAP) whose datasets are combined into a baseline map and sampled to train a Random Forest algorithm based on climatic and geographic data. Their evaluation against test data from the baseline maps set aside for validation purposes show a good performance with SOS (EOS) R 2 of 0.87 (0.92) and a root mean square error (RMSE) of 27 (26) days for wheat, showing the lowest errors (RMSE <15 days) in North America, Central Europe, South Africa, and Australia, all critical areas for global wheat production and trade. Meanwhile, the largest errors (RMSE between 40 and 60 days) occurred in regions of South America close to the Amazon Forest and in Africa close to the Congo Basin. In the case of maize, the SOS (EOS) evaluation shows an R 2 of 0.88 (0.79) and an RMSE of 24 (28) days for maize, with the best performing regions (RMSE < 15 days) located in the Northern Hemisphere, South Africa, and Australia, important areas for global maize production and trade. Meanwhile, the worst performing regions were in Brazil, Saudi Arabia and India. Additionally, the crop calendars were evaluated using a simple Land Surface Phenology (LSP) model based on Sentinel-2 and Landsat 8 Earth Observation data from Sentinel-2 and Landsat 8 over known wheat and maize fields. The results show a SOS (EOS) R 2 of 0.75 (0.88) and an RMSE of 25 (18) days for wheat and SOS (EOS) R 2 of 0.80 (0.88) and an RMSE of 35 (24) days for maize. Therefore, the presented calendars present an advancement over the existing crop calendar products in terms of capturing spatial coverage and variability and reporting their accuracy.

HAOR AREA PEOPLES’ ADAPTATION STRATEGIES TO CLIMATE CHANGE INDUCED EVENTS IN KISHOREGANJ, BANGLADESH

Background: Bangladesh is a vulnerable country with respect to climate change especially in Haor areas because of its geographic location, lateral of flood plains, high population density, overhead level of poverty and irresistible dependency on nature. An extreme weather events due to climate change pose a risk to future food security. Objectives: This study analyzed the haor area people’s adaptation ability and significant factors influencing their adaptation towards climate change. Methods/Approach: Various descriptive statistical analysis and frequency distribution were used to examine the status of various adaptation strategies and village-wise binary logistic regression analysis was performed to determine the factors that significantly affects haor area people’s adaptation to climate change. Results: The people of Khoishore, Dalargaon and Hasimpur villages adopted different adaptation strategies like take loan, seasonal migration, job switching, and changing crop calendar etc. taken because of climate change induce events such as flash flood, riverbed fill up and riverbank erosion. Education level, possession of own farming land, yearly family income, perception level of people about climate change impact are found most significant factors that motivated people to take various adaptation strategies. Conclusions: Government and NGO’s should come forward to arrange alternative income opportunities for the people and to build up sustainable flood control embankments to prevent the damage of flash flood in haor area due to climate change induce events.

An Appraisal of Legal Framework for Groundwater Governance in Punjab

Groundwater (GW) in India is emerging as the major source of water which contributes about 85% to drinking water supply in rural areas and 62% to irrigation. In Punjab, GW provides irrigation to more than 72% of the area under the rice-wheat cropping system and it has played a key role in its emergence as 'granary of India’. But the area under rice, a water-guzzling crop grown in the state during summer, has increased tremendously, i.e., from 3.90 lac hectares in 1970-71 to 31.03 lac hectares in 2018-19. The runaway growth of GW irrigation has also contributed to the depletion of the water table and thereby posing a huge environmental challenge. In this paper, an attempt has been made to review the development of the legal framework for GW governance and its effect on the groundwater situation in the state. However, the increasing stress on aquifers due to GW irrigation has been reduced to an acceptable degree by strengthening and enforcement of legal framework coupled with a set of incentives and disincentives for improving its efficiency. The Punjab Preservation of Sub-soil Water Act 2009 coupled with some minor technical interventions have contributed to a reduction in the consumption of irrigation water by 413liters per kg of production of rice due to a change in the crop calendar of rice and following the wheat. It is being increasingly acknowledged that for effective GW governance in Punjab, science and policy for GW use need to ?ank and complementsthe legal frameworks.

High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine

Rice is the staple crop for more than half the world’s population, but there is a lack of high-resolution maps outlining rice areas and their growth stages. Most remote sensing studies map the rice extent; however, in tropical regions, rice is grown throughout the year with variable planting dates and cropping frequency. Thus, mapping rice growth stages is more useful than mapping only the extent. This study addressed this challenge by developing a phenology-based method. The hypothesis was that the unsupervised classification (k-means clustering) of Sentinel-1 and 2 time-series data could identify rice fields and growth stages, because (1) the presence of flooding during transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. Using the proposed method, this study mapped rice field extent and cropping calendars across Peninsular Malaysia (131,598 km2) on the Google Earth Engine (GEE) platform. The Sentinel-1 and 2 monthly time series data from January 2019 to December 2020 were classified using k-means clustering to identify areas with similar phenological patterns. This approach resulted in 10-meter resolution maps of rice field extent, intensity, and cropping calendars. Validation using very high-resolution street view images from Google Earth showed that the predicted map had an overall accuracy of 95.95%, with a kappa coefficient of 0.92. In addition, the predicted crop calendars agreed well with the local government’s granary data. The results show that the proposed phenology-based method is cost-effective and can accurately map rice fields and growth stages over large areas. The information will be helpful in measuring the achievement of self-sufficiency in rice production and estimates of methane emissions from rice cultivation.

Analyzing farm households' perception and choice of adaptation strategies towards climate change impacts: a case study of vulnerable households in an emerging Asian region.

Poor agricultural communities are particularly more disruptive to changes in climate. In southeast Asian countries, Pakistan is extremely vulnerable to weather-related events including droughts and floods. This research study aims to determine the farmers' perception towards climatic risks, farmers' choice of adaptation strategies and factors influencing farmers' decision of adaptation measures. A face-to-face household survey was conducted to collect primary data of 378 farm households from three tehsils of district Charsadda-Khyber Pakhuntkhwa (KP) province of Pakistan. A structured questionnaire was utilized as an instrument to collect data from the targeted farmers in study areas (three tehsils of district Charsadda). A binary probit model approach was used to assess the major factors affecting farmers' decision towards adaptation measures. Results revealed that changing crop varieties, diversification of crops, changing crop calendar, and insurance of crops were the major adaptive measures exercised by farm households in their farms. Findings of the binary probit approach showed that age of the respondents, farm size, educational level, credit access, household size, extension services access and perception of increased floods, and reduction in precipitation had substantial effect on the farmers' adaptation strategies choice. Advanced agricultural practices in response to the climatic risks can thus have substantial effects and reduction in farmers' exposure to natural calamities. Study findings of our research can guide policy makers and concerned authorities and provide policy implications for future research studies.

Improving Supply Chain Planning Management Responsiveness and Agility: FMCG Company Case

In today’s progressively competitive market, a precise and clear evaluation of the supply planning management is necessary to seek the protection of the market position as well as the market shares from expected and unexpected future threats. This paper presents the methodology of evaluating and selecting the most appropriate methods to improve the Supply chain Planning Management Responsiveness and Agility by implementing E2E LT, Production Capacity, SS and DOC, OTIF, Planning Segmentation, Planning Zones, and Crop Calendar.

Retrospective Predictions of Rice and Other Crop Production in Madagascar Using Soil Moisture and an NDVI-Based Calendar from 2010–2017

Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 2020–2021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet we are unaware of peer-reviewed studies that quantitatively link variations in weather and climate to agricultural outcomes for staple crops in Madagascar. In this study, we use historical data to empirically assess the relationship between soil moisture and food production. Specifically, we focus on major staple crops that form the foundation of Malagasy food systems and nutrition, including rice, which accounts for 46% of the average Malagasy caloric intake, as well as cassava, maize, and sweet potato. Available data associated with survey-based crop statistics constrain our analysis to 2010–2017 across four clusters of Malagasy districts. Strong correlations are observed between remotely sensed soil moisture and rice production, ranging between 0.67 to 0.95 depending on the cluster and choice of crop calendar. Predictions are shown to be statistically significant at the 90% confidence level using bootstrapping techniques, as well as through an out-of-sample prediction framework. Soil moisture also shows skill in predicting cassava, maize, and sweet potato production, but only when the months most vulnerable to water stress are isolated. Additional analyses using more survey data, as well as potentially more-refined crop maps and calendars, will be useful for validating and improving soil-moisture-based predictions of yield.

Estimación de la producción de cebada a partir de imágenes Sentinel-1, Sentinel-2 y variables climáticas

A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.