Search

This paper focuses on the weed species Echinochloa crus-galli, commonly known as barnyardgrass, which is a persistent threat to crop yield and quality, especially in maize (Zea mays) cultivation. It is one of the most problematic weeds in agricultural fields due to its aggressive growth, adaptability to different environmental conditions and prolific seed production. The incidence of Echinochloa crus-galli in maize fields has increased in recent years. This study aims to provide a comprehensive understanding of the characteristics and behavior of Echinochloa crus-galli, and to suggest effective measures to control it. This research on sweetcorn was conducted from 1992 to 2019 at the Research and Education Center Gorzyń, Złotniki branch, which belongs to the Poznań University of Life Sciences. The evaluation of weed infestation was carried out in experiments focusing on chemical weed control in maize. The experiments were designed as a one-factor randomised block design with four field replications. The condition and the degree of weed infestation (number of weeds and fresh weight of weeds) in the control plots was assessed on an annual basis at the end of June and in July. The aim of the study was to evaluate the dynamic changes in the status and extent of barnyardgrass (Echinochloa crus-galli) infestation in maize grown after various other crops in the Wielkopolska region, with a focus on the weather conditions. The study found that barnyardgrass was most likely to occur when maize was sown after winter wheat in a dry and warm year, and least likely when maize was grown in rotation after winter wheat in a cold year with average rainfall. The proportion of barnyardgrass weed mass in the total weed mass was significantly lower after winter rye than after winter wheat, winter oilseed rape and winter triticale. Further research into the biology and ecology of barnyardgrass is key to effectively controlling this weed and safeguarding sweetcorn yields. [ABSTRACT FROM AUTHOR]

Heat pulse method is a transient method that estimates soil thermal properties by characterizing the radial transport of short-duration line-source heat applied to soils. It has been widely used to measure a wide range of soil physical properties including soil thermal conductivity, thermal diffusivity, heat capacity, water content, ice content, bulk density, water flux and evaporation in laboratory and field environments. Previous studies generally focus on the scientific aspects of heat pulse method based on selected publications, and there is a lack of study investigating the heat pulse publication as a whole. The objective of this study was to give an overall view of the use of heat pulse method for soil physical measurements from the bibliometric perspectives. The analyses were based on the Web of Science Core Collection data between 1992 and 2019 using HistCite Pro and VOSviewer. The results showed an increasing trend in the volume of publications on this field and Dr. Robert Horton was the most productive researcher coauthoring papers on the heat pulse method. The co-authorship analysis revealed that researchers from soil science are closely collaborated, but this is not true for researchers in other fields. There is a lack of new young scientists committing to this field while the older generation of researchers are retiring. The United States Department of Agriculture Agricultural Research Servics (USDA-ARS), the China Agriculture University and the Chinese Academy of Science were the top three organizations applying the heat pulse method, while the USA, China and Canada were the top three countries. The Soil Science Society of America Journal, Water Resources Research and Agricultural and Forestry Meteorology were the most widely used journals. The con-occurrence and citation analysis could be used to map the development of the field and identify the most influential publications. The study showed that the bibliometric analysis is a useful tool to visualize research status as well as to provide the general information of novices and experts alike on the heat pulse method for soil physical measurements. [ABSTRACT FROM AUTHOR]

The variety of natural indicators, associated with weather forecasting and climate prediction, as used by farmers in the South-Western Free State province of South Africa, is described. Most farmers in this area were not familiar with the application of weather forecasts/climate predictions for agricultural production, or with other science-based agrometeorological products. They relied almost fully on their experience and traditional knowledge for farming decision making. The indicators for traditional knowledge are demonstrated here in broad terms, relying on the stories and indications from observations and years of experience of their use by the farmers. These means of engagement with the natural environment, are skills not well understood by most scientists, but useful to the farmers. They range from the constellation of stars, animal behavior, cloud cover and type, blossoming of certain indigenous trees, appearance and disappearance of reptiles, to migration of bird species and many others. It is suggested that some short-term traditional forecasts/predictions may be successfully merged with science-based climate predictions. The traditional knowledge and its use, reported on in this paper, is what scientists learned from farmers. Berkes was right that scholars have wasted too much time and effort on a science versus traditional knowledge debate; we should reframe it instead as a science and traditional knowledge dialogue and partnership. The complications of a changing climate make this even more necessary. [ABSTRACT FROM AUTHOR]

Extension agrometeorology is applied in agrometeorological extension work to advice and serve farmers. In agrometeorology, response farming has been developed decades ago. Climate change complicates response farming, but does not alter it. This paper reports on new operationalization of that response farming in new educational commitments in agroclimatology. It is explained how "Science Field Shops" are an example in Indonesia. This was based on a thorough analysis of what climate change means for farmers in Asia. For Africa, we report on eying the training of agrometeorological extension trainers ("product intermediaries") in West Africa, based on a thorough analysis of what climate change means for farmers in Africa. We also compare experience with reaching farmers in South Africa and farmer communities in Zambia, as new forms of supporting response farming, all under conditions of a changing climate. The paper, for the first time, connects results from four different programs the senior author is taking part in. There is first and foremost the need for training material to make it possible for the product intermediaries to participate in training extension intermediaries. This should, particularly, bring new knowledge to farmers. With what is presently available and with new approaches, climate extension should be developed and tested with farmers in ways that improve farmer preparedness and decision making. [ABSTRACT FROM AUTHOR]

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies. [ABSTRACT FROM AUTHOR]

Historically, the olive grove has been one of the most emblematic ecosystems in Mediterranean countries. Currently, in Andalusia, Spain, the land under olive grove cultivation exceeds 1.5 million hectares, approximately 17% of the regional surface. Its exploitation has traditionally been based on the use of the available land and heterogeneous plantations, with different species adapted to southern Mediterranean climatic conditions, and to the management of the traditional olive cultivation culture. The objective of this work is to characterize the mechanical behavior of the atmospheric surface boundary layer (SBL) (under neutral stability) interacting with different olive grove configurations. Experimental tests were carried out in the Boundary Layer Wind Tunnel (BLWT) of the Andalusian Institute for Earth System Research (IISTA), University of Granada. Three representative configurations of olive groves under neutral atmospheric conditions were tested. The wind flow time series were recorded at several distances and heights downwind the olive plantation models with a cross hot wire anemometry system. Herein, this paper shows the airflow streamwise, including the mean flow and the turbulent characteristics. The spatial variability of these two mechanical magnitudes depends on, among others, the size, the agro-forest length, the layout of the tree rows, the porosity, the tree height, the crown shape and the surface vegetation cover. The aerodynamic diameter and Reynolds number for each agro-forest management unit are proposed as representative variables of the system response, as these could be related to olive grove management. The plantation, in turn, conforms to a windbreak, which affects the microclimate and benefits the elements of the ecosystem. Detailed knowledge of these variables and the interaction between the ecosystem and the atmosphere is relevant to optimize the resources management, land use and sustainability of the overall crop. Thus, this paper presents preliminary work to relate atmospheric variables to environmental variables, some of which could be humidity, erosion, evapotranspiration or pollen dispersion. [ABSTRACT FROM AUTHOR]

The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical ("event scenario") approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs. [ABSTRACT FROM AUTHOR]

In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the "early warning service for weather risk management in the agricultural sector" that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology. [ABSTRACT FROM AUTHOR]

Changes in climatic conditions increase risks associated with crop production in certain regions. Early detection of these changes enables the implementation of suitable adaptation measures in the local area, thereby stabilising agricultural production. Our analysis shows a significant shift in climatic conditions in Czechia between 1961 and 2020. We examined the changes in observed temperature conditions, precipitation distribution, drought occurrences, and frost incidents at a high resolution (0.5 × 0.5 km). The outputs show a significant increase in air temperatures and drought occurrence. Temperature totals above 5 °C in 1991–2020 were 15% higher than in 1961–1990. Furthermore, the relative change in totals above 10 °C was 26% after 1991. Over the last 30 years, drought incidence was four times more frequent than in 1961–1990, particularly in spring. In contrast, no significant changes in the distribution of precipitation occurred, and there was a slight decrease in the probability of frost during the growing season. Ongoing climate change brings warmer and drier conditions to higher-altitude regions in Czechia. Assessing climatic conditions on a global scale is less precise for relatively small and topographically diverse countries like Czechia due to coarse resolution. Therefore, a high-resolution analysis is more appropriate for these countries. [ABSTRACT FROM AUTHOR]

Timely sowing and harvesting play important roles in agricultural production. The appropriate management decisions are necessary to cope with climate change and ensure high and stable crop yields. This study analyzed the effects of sowing date on the growth process of winter wheat and quantified the effects of climate resources and photothermal potential yield on theoretical yield at different stages of winter wheat. The analysis was based on the data from winter wheat interval sowing experiments conducted at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station (Gucheng station) in north China (115°40′ E, 39°08′ N) during 2017–2019. The results showed that: (1) with the delay in sowing date, the growth process of winter wheat significantly advanced, the proportion of vegetative growth period significantly reduced (0.19% for per day delay), the proportion of reproductive growth period (RGP) significantly increased (0.12% for per day delay), and the prewintering light and temperature resources significantly reduced (12.2 °C·d accumulated temperature and 19.0 MJ·m−2 solar radiation for per day delay); (2) the theoretical yield of winter wheat showed a significant exponential relationship with the photothermal potential yield of the whole growth period: the minimum photothermal potential for yield formation was 26.6 t·ha−1, and the maximum theoretical yield was 12.6 t·ha−1; and (3) the wheat yield and yield stability were highest when the RGP photothermal potential yield was 16.0 t·ha−1 and the prewintering active accumulated temperature was 400 °C·d. This study also proposed a method to estimate the suitable sowing and harvesting dates to achieve high and stable yield of winter wheat, showing that the suitable sowing dates of winter wheat at Gucheng station from 1997 to 2021 ranged from 1 to 15 October, with no significant interannual variation; the suitable harvesting period ranged from 5 June to 10 July and showed a trend of gradual advance with the delay of the year. The results of the study provide a reference for sowing date adjustment of crops to adapt to climate change. [ABSTRACT FROM AUTHOR]

In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-testing template for future droughts. The analysis connects weather and crop yield data to impacts on local macroeconomic conditions by comparing drought-impacted agricultural counties with nearby urban counties. After measuring the net macroeconomic impacts of the drought, this was used as an overlay with existing macroeconomic stress models to stress test a lender in a different part of the US for possible drought impacts. Having a library of such climate events would allow lenders to stress test their portfolios for a wide range of possible impacts. [ABSTRACT FROM AUTHOR]

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. [ABSTRACT FROM AUTHOR]

South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences. [ABSTRACT FROM AUTHOR]

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year. [ABSTRACT FROM AUTHOR]

Regional estimations of Potential Evapotranspiration (PET) are of key interest for a number of geosciences, particularly those that are water-related (hydrology, agrometeorology). Therefore, several models have been developed for the consistent quantification of different time scales (hourly, daily, monthly, annual). During the last few decades, remote sensing techniques have continued to grow rapidly with the simultaneous development of new local and regional evapotranspiration datasets. Here, we develop a novel set T maps over the globe, namely RASPOTION, for the period 2003 to 2016, by integrating: (a) mean climatic data at 4088 stations, extracted by the FAO-CLIMWAT database; (b) mean monthly PET estimates by the Penman–Monteith method, at the aforementioned locations; (c) mean monthly PET estimates by a recently proposed parametric model, calibrated against local Penman–Monteith data; (d) spatially interpolated parameters of the Parametric PET model over the globe, using the Inverse Distance Weighting technique; and (e) remote sensing mean monthly air temperature data. The RASPOTION dataset was validated with in situ samples (USA, Germany, Spain, Ireland, Greece, Australia, China) and by using a spatial Penman–Monteith estimates in England. The results in both cases are satisfactory. The main objective is to demonstrate the practical usefulness of these PET map products across different research disciplines and spatiotemporal scales, towards assisting decision making for both short- and long-term hydro-climatic policy actions. [ABSTRACT FROM AUTHOR]

To achieve sustainable development and to ensure water availability in hydrological basins, water managers need tools to determine the actual evapotranspiration (ET) on a large scale. Field energy balances from irrigated and natural ecosystems together with a net of agro-meteorological stations were used to develop two models for ET quantification at basin scale, based on the Penman-Monteith equation. The first model (PM1) uses the resistances to the latent heat fluxes estimated from satellite measurements, while the second one (PM2) is based on the ratio of ET to the reference evapotranspiration (ET0) and its relation to remote sensing parameters. The models were applied in the Low-Middle São Francisco river basin in Brazil and, after comparison against field results, showed good agreements with PM1 and PM2 explaining, respectively, 79% and 89% of the variances and mean square errors (RMSE) of 0.44 and 0.34 mm d-1. Even though the PM1 model was not chosen for ET calculations, the equation for surface resistance (rs) was applied to infer the soil moisture conditions in a simplified vegetation classification. The maximum values of rs were for natural vegetation—caatinga (average of 1,937 s m-1). Wine grape and mango orchard presented similar values around 130 s m-1, while table grape presented the lowest ones, averaging 74 s m-1. Petrolina and Juazeiro, in Pernambuco (PE) and Bahia (BA) states, respectively, were highlighted with the biggest irrigated areas. The highest increments are for vineyards and mango orchards. For the first crop the maximum increment was verified between 2003 and 2004 in Petrolina-PE, when the cultivated area increased 151%. In the case of mango orchards the most significant period was from 2005 to 2006 in Juazeiro-BA (129%). As the best performance was for PM2, it was selected and used to analyse the regional ET at daily and annual scales, making use of Landsat images and a geographic information system for different soil moisture conditions. Considering the daily rates of the regional ET, pixels with values lower than 1.0 mm d-1 occurred outside the rainy season, representing the caatinga species. Values from 1.0 to 5.0 mm d-1 during the driest conditions of the year coincided with irrigated crops, being the highest values for table grapes. The highest accumulated ET values during 2006 were for mango orchards, being around 500-1,300 mm yr-1. Vineyards presented lower values, ranging from 450-800 mm yr-1, while in caatinga they were between 200 and 400 mm yr-1. It could be concluded that irrigated mango orchards and vineyards in that year consumed more water than caatinga by factors of 3 and 2, respectively. The mango orchards and vineyard areas, representing 19.4 and 8.2% of the total irrigated area, respectively, resulting in a total evaporative depletion of 0.22 km3 yr-1 in the growing regions comprised of the agro-meteorological stations. [ABSTRACT FROM AUTHOR]

Often in agrometeorology the instrumentation required to estimate turbulent surface fluxes must be installed at sites where fetch is not sufficient for a sector of wind directions. For different integrated flux-footprints (IFFP) thresholds and taking as a reference the half-hourly latent heat fluxes (LE) measured with a large weighing lysimeter (LELys), the eddy covariance (EC) method and two methods based on surface renewal (SR) analysis to estimate LE were tested over short fescue grass. One method combined SR with the flux-gradient (profile) relationship, SR-P method, and the other with the dissipation method, SR-D method. When LE was estimated using traces of air moisture, good performances were obtained using the EC and the SR-P methods for samples with IFFP higher than 85%. However, the closest LE estimates were obtained using the residual method. For IFFP higher than 50%, the residual method combined with the sensible heat flux estimates determined using the SR-P method performed close to LELys and using the SR-D method good estimates were obtained for accumulated LELys. To estimate the sensible heat flux, the SR-D method can be recommended for day-to-day use by farmers because it is friendly and affordable. [ABSTRACT FROM AUTHOR]

Changes in climate, land use, and population growth has put immense pressure on the use of water resources in agriculture. Non-irrigated fields suffer from variable water stress, leading to an increase in the implementation of irrigation technologies, thus stressing the need to analyze diverse irrigation practices. An evaluation of 17 sites in the U.S. Corn Belt for two temporal climatic conditions was carried out. It consisted of the analysis of critical hydroclimatic parameters, and the evaluation of seven diverse irrigation strategies using the Deficit Irrigation Toolbox. The strategies included rainfed, full irrigation, and several optimizations of deficit irrigation. The results show a significant change in the hydroclimatic parameters mainly by increased temperature and potential evapotranspiration, and a decrease in precipitation with an increase in intense short rainfall events. Consequently, the simulations indicated the potential of deficit irrigation optimization strategies to increase water productivity above full irrigation and rainfed conditions. In particular, GET-OPTIS for wet soil conditions and the Decision Tables for dry soil conditions seasons. The present study highlights the contributions of atypical weather to crop production and the implications for future management options, and allows specialized regionalization studies with the optimal irrigation strategy. [ABSTRACT FROM AUTHOR]

Establishing food security in sub-Saharan African countries requires a comprehensive and high resolution understanding of the driving factors of crop production. Poor soil and adverse climate conditions are among the major drivers of poor regional crop production. Drought and rainfall variability challenges are not fully being addressed by rainfed producers in semiarid areas. In this study, we analysed the spatiotemporal rainfall variability (STRV) and its effects on pearl millet yield using two seasons of data collected from 38 rain gauge stations scattered randomly in farm plots within a 1500 ha area of semiarid central Tanzania. The STRV effects on pearl millet yield under flat and tied ridge management were analysed. Our results show that seasonal rainfall can vary significantly for neighboring fields at distances of less than 200 m, which impacts yield. The STRV for daily rainfall was found to be more critical than for total seasonal rainfall amounts. Scattering fields can help farmers avoid total harvest loss by obtaining at least some yield from the areas that received adequate rain. The use of tied ridges is recommended to conserve soil moisture and improve yields more than flat cultivation in semiarid areas. [ABSTRACT FROM AUTHOR]

Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial resolutions, i.e., GFSAD1km, GFSAD250m, and GFSAD30m. An accuracy assessment and comparison of these three GFSAD cropland extent maps was performed to establish their quality and reliability for monitoring croplands both at global and regional scales. Large area (i.e., global) assessment of GFSAD cropland extent maps was performed by dividing the entire world into regions using a stratification approach and collecting a reference dataset using a simple random sampling design. All three global cropland extent maps were assessed using a total reference dataset of 28,733 samples. The assessment results showed an overall accuracy of 72.3%, 80–98%, and 91.7% for GFSAD1km, 250 m (only for four continents), and 30 m maps, respectively. Additionally, a regional comparison of the three GFSAD cropland extent maps was analyzed for nine randomly selected study sites of different agriculture field sizes (i.e., small, medium, and large). The similarity among the three GFSAD cropland extent maps in these nine study sites was represented using a similarity matrix approach and two landscape metrics (i.e., Proportion of Landscape (PLAND) and Per Patch Unit (PPU)), which categorized the crop proportion and the crop pattern. A comparison of the results showed the similarities and differences in the cropland areas and their spatial extent when mapped at the three spatial resolutions and considering the different agriculture field sizes. Finally, specific recommendations were suggested for when to apply each of the three different GFSAD cropland extent maps for agriculture monitoring based on these agriculture field sizes. [ABSTRACT FROM AUTHOR]