Agrometeorological Models Research Articles

Agrometeorological modeling to estimate soybean productivity in the Southern Cone of Rondônia

Agrometeorological modeling is crucial to predict the impact of climatic conditions on agricultural production. However, its application in large regions faces challenges due to the lack of weather stations and limited access to real-time data. In this study, the multiplicative agrometeorological model was used to estimate soybean productivity in the seven municipalities of the Southern Cone of Rondônia in the 2020/21 to 2022/23 harvests. This model considers the relationships between actual and potential evapotranspirations and water conditions during different phenological stages. Ten-year water balances were developed for each municipality, using air temperature data from ERA5-Land from the ECMWF and precipitation data estimated by NASA’s GPM_v06 satellite. The results showed satisfactory performance of the model, with coefficients of determination ranging from 0.48 to 0.88 and agreement indices from 0.41 to 0.90 compared to the data observed by IBGE.

Revisiting Climate-Related Agricultural Losses across South America and Their Future Perspectives

Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. In recent years, remote sensing data and geographic information systems have been used to improve geo-environmental hazard assessment. However, food security is still highly dependent on small farmer practices that are frequently the most vulnerable to climate extremes. This work reviews climate and weather extremes’ impacts on crop production for South American countries, focusing on the projected ones considering different climate scenarios and countries. A positive trend in the productivity of maize, mainly related to agricultural improvements, was recently observed in Colombia, Ecuador, and Uruguay by up to 200%, as well as in the case of soybean in Bolivia and Uruguay by about 125%. Despite the generalized adverse impacts of climate extremes, results from agrometeorological models generally indicate an increase in crop production in southern regions of Chile (and highlands) and Brazil mainly related to increased temperature. Positive impacts in response to CO2 fertilization are also foreseen in Peru and Brazil (southeast, south, and Minas Gerais); in particular, in Brazil, increases in productivity can be raised by about 40%. The use of double-cropping systems, although with very good results in recent years, may also be at risk in a few decades, mainly due to forecasted precipitation decrease, delay in rainy season onset, and temperature increase. The development of timely early warning systems is imperative to produce technically accurate alerts and the interpretation of the risk assessment based on the link between producers and consumers. Promoting climate index insurance is crucial to build resilient food production, but its implementation should rely on regional or international support systems. Moreover, the implementation of adaptation and mitigation also requires climate-resilient technologies that involve an interdisciplinary approach.

Cover crop water consumption: Analysing performance of the agrometeorological model for the calculation of actual evapotranspiration (AMBAV) in a container experiment

AbstractDue to anthropogenic climate change, cover crop water consumption in winter could potentially increase drought stress for a succeeding crop. Simulation of cover crop evapotranspiration (ET) losses could be a tool for farmers to make smart management decisions. In Germany, the model AMBAV is used by the German Meteorological Service (DWD) to advise farmers in irrigation management. We compared measured ET of phacelia (Phacelia tanacetifolia), oilseed radish (Raphanus sativus var. oleiformis) and white mustard (Sinapis alba) cultivated in a container experiment with simulated data and conducted a sensitivity analysis to identify the meteorological and crop‐specific parameters, which had the strongest effect on simulated ET. In general, measured ET exceeded simulated ET. Different statistical criteria showed that AMBAV performed best for the simulation of evaporation from a bare soil surface. Model performance was also strongly influenced by the irrigation regime in the container experiment. However, the sensitivity analysis showed that changes in irrigation hardly influenced simulated ET. We recommend optimization of the model for irrigated agriculture. Furthermore, we identified temperature and humidity as the most important meteorological and leaf area index as the most important crop‐specific parameter for ET simulations with AMBAV. Since farmers' management decisions depend on the accuracy of ET simulations, they should be aware that even small regional deviations of meteorological conditions and soil cover can significantly affect model predictions.

Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil?

Agrometeorological models are great tools for predicting yields and improving decision-making. High-quality climatic data are essential for using these models. However, most developing countries have low-quality data with low frequency and spatial coverage. In this case, two main options are available: gathering more data in situ, which is expensive, or using gridded data, obtained from several sources. The main objective here was to evaluate the quality of two gridded climatic databases for filling gaps of real weather stations in the context of developing agrometeorological models. Therefore, a comparative analysis of gridded database and INMET data (precipitation and air temperature) was conducted using an agrometeorological model for sweet orange yield estimation. Both gridded databases had high determination and concordance coefficients for maximum and minimum temperatures. However, higher errors and lower confidence coefficients were observed for precipitation data due to their high dispersion. BR-DWGD indicated more accurate results and correlations in all scenarios evaluated in relation to NasaPower, pointing out that BR-DWGD may be better at filling gaps and providing inputs to simulate attainable yield in the Brazilian citrus belt. Nevertheless, due to the BR-DWGD database’s geographical and temporal limitations, NasaPower is still an alternative in some cases. Additionally, when using NasaPower, it is recommended to use a measured precipitation source to improve prediction quality.

Diagnosis of the rainfall-wheat yield relationship in the current and future climate change conditions in Eastern Algeria

Future projections indicate that rain-fed agriculture in North Africa is among the most vulnerable in the world in the context of future climate change. This article aims to diagnose the relationship between rainfall and wheat yield in both current and future climatic situations in a semi arid agro-climatic conditions represented by the region of Bordj Bou Arreridj. For the current situation, we used 15 years (1995–2009) of recorded rainfall and durum wheat yield series. Future rainfall projections (2071–2100) were generated by the MED-CORDEX climate model version CCLM4-8-19 under RCP 6.0 scenario. Simulated data over the observed period and that of the future on the maximum evapotranspiration (ETM) of durum wheat and the water deficit (WD) accumulated over the cycle as well as future yields are obtained using a simple agro meteorological crop simulation model, previously validated. In both current and future situations, precipitations, ETM, WD and yields data are first analyzed, then yields are related by regression to three components of rainfall: annual rainfall, cumulative rainfall over the crop cycle (November–June) and cumulative rainfall during spring (March–May). In the observed climate, annual precipitation averages 382.3 ± 96.3 mm, cumulative rainfall over the crop cycle (November–June) averages 278.3 mm and cumulative rainfall during spring is 101.9 mm. These last decrease to 303.7 ± 99.4, 232.3 and 83.3 mm in the future situation. Observed yields (1995–2009) averages1.9 ± 0.64 q/ha in the observed situation and decrease to 15.5 ± 0.54 q/ha in future climate. ETM are low and WD values are high in the current climate, with a worsening of the situation in the future climate, particularly during spring. The correlation between yields and précitations is always positive in both weather conditions, but the best R2 are 0.65 and 0.82 and concern spring rains. In semi-arid regions, cumulative rainfall towards the end of the growing season is currently impacting the grain yield of durum wheat and will become more decisive in the context of future climate change.

Applying the CROPGRO Perennial Forage Model for long-term estimates of Marandu palisadegrass production in livestock management scenarios in Brazil

The use of long-term forage production simulation is important to studies of strategic scenarios, yield-gap predictions and production risk analysis. However, forage models have only been tested using short period data. Therefore, this study aimed to apply the CROPGRO Perennial Forage Model (CROPGRO-PFM), a mechanistic model from Decision Support System for Agrotechnology Transfer (DSSAT) platform, to simulate long-term forage production of Marandu palisadegrass {Urochloa brizantha (Hochst. ex A. Rich.) R.D. Webster [syn. Brachiaria brizantha (Hochst. ex A. Rich.) Stapf]} in different locations of Central Brazil, under different fertilization levels. We chose nine locations distributed in Amazon, Cerrado, and Atlantic Forest Biomes to simulate Marandu palisadegrass production over 36 years (1980–2016). The simulations were carried out using prior calibrations for DSSAT’s CROPGRO-PFM and, as a reference point of growth and herbage simulation, model results were initially contrasted against an agrometeorological model, the Integrated Agrometeorological Model (IAM), to check the consistency between these two different models. Then the CROPGRO-PFM was applied to simulated scenarios represented three beef cattle livestock production systems: (1) Potential production water-not-limiting and highly fertilized, (2) rainfed with high N fertilization, and (3) rainfed with low N maintenance fertilization. In general, the long-term simulations of annual forage production and seasonality were similar for the CROPGRO-PFM and IAM models. As expected for the Central Brazil region, historical averages of monthly herbage accumulation rate of potential yield scenarios were relatively constant throughout the year, declining in the winter period for most locations due to mild temperature reduction and decline of solar radiation. In general, rainfed scenarios simulations showed a seasonal cycle following the rainfall pattern, with reduction in growth rates in the dry season. During the winter season climate is the major limiting growth factor, simulated growth rates were low and similar regardless of N fertilization. Although the CROPGRO-PFM presented consistent results for long term grazing systems, further model changes, calibration, and validations are recommended to improve the process-based model as well as the knowledge about these systems.

Thermal requirements and agrometeorological modeling of cotton growth under soil fertility levels

The objective was to quantify the thermal requirements and model the growth of cotton as a function of air temperature and degree-days, under levels of soil saturation and potassium fertilization, grown in a protected environment, in Bom Jesus, Piauí. The experimental design used was a completely randomized design (DIC) with a 3 X 3 + Witness factorial scheme, consisting of three levels of base saturation (V%) (50%, 60% and 70%) and three concentrations of potassium fertilization doses (50% below recommended; recommended, according to soil analysis; 50% above recommended) and Control, without K application, with four replications. Plant height, stem diameter, number of nodes, midrib length and leaf area were evaluated. The meteorological data were obtained in a portable meteorological station, installed inside the protected environment. Among the stages, the First Boll-Harvestpresented the highest thermal demand, with an average of 551.7 °gd and 34 days of duration, followed by the Emergence-First Floral Bud (435.6 °gd), First Floral Bud- First Flower (362.5 °gd) and First Flower-First Boll (300.3 °gd), the Sowing-Emergence phase obtained 51.4 °gd. Air temperature is the meteorological element with the greatest impact on cotton development.

Coffee pest severity by agrometeorological models in subtropical climate.

This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the (Camargo Campinas IAC Boletim 116:9, 1971) method and the water balance was calculated using the method of ( Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA). The basal temperature of each pest minus the average temperature of the years was used to calculate the degrees-day, the duration of the pest cycle, and the number of generations per year. The influence of altitude on the development of coffee pests was measured using the Pearson correlation. The thermal index is able to estimate the damage caused by coffee pests in the state of Pará, Brazil. Coffee pests show greater severity in the north of Paraná, in the regions with the highest temperatures. It is the same region that concentrates most of the coffee production of the state. The results of the life cycle and number of generations were interpolated for the entire state using the kriging method. Coffee pests showed the highest severity in the north region of the state of Paraná, more specifically in the Northwest, North Central, and West Central mesoregions. These regions have concentrated most of the state's coffee production. Mesoregions with the highest coffee production in the state showed higher susceptibility to coffee pests. Altitude showed a high correlation (r > 0.6) with the cycle variability and number of generations of coffee pests. The average cycles of the coffee berry borer, coffee leaf miner, and southern red mite are 24.13 (± 8.34), 45.64 (± 18.61), and 21.51 (± 3.51) days, respectively. The average annual generation was 16.67 (± 4.77), 9.02 (± 2.75), and 17.32 (± 2.63) generations, for the coffee berry borer, the coffee red mite, and the southern red mite, respectively.

Agrometeorological models for predicting seedlings development of two native forest species

Models of development are tools that connect the effects of development on the environment, allowing their applications in several studies. Nevertheless, studies are scarce on models of development for native forest species in Brazil. This study aimed to predict the development of two native forest species - Citharexylum myrianthum Cham. and Bixa orellana L. - with two agrometeorological models, being one linear (Phyllochron) and another nonlinear (Wang and Engel, 1998). Both models predict the cumulative leaf number (CLN) on a daily basis, which generates the seedling phase duration (SPD) when integrated to time. Data were used from two years of experiments conducted during 2015 and 2016 growing seasons and 12 sowing dates in Itajubá, Minas Gerais State, Brazil. These species × sowing dates × years experiments provided a rich dataset for calibrating and evaluating both models. Although both models used in the study allowed predicting the dynamics of leaf development, CLN, and SPD in two native forest species, the Wang and Engel model provided a more accurate prediction of CLN and SPD for C. myrianthum species, with an overall root mean square error (RMSE) of 1.82 leaves (CLN) and 5.9 days (SPD). For B. orellana, the Phyllochron model was slightly better, with an overall RMSE of 1.48 leaves (CLN) and seven days (SDP).

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Can nonlinear agrometeorological models estimate coffee foliation?

The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987-2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four-parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees-day for 70 locations. The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error=2.19%) and precise (R2 adj =0.99) and can be used to assist decision-making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax =97.63%, ymin =9%, Xo =3517.41 DD, and p=6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of -0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry.

Simulation of Daily Mean Soil Temperatures for Agricultural Land Use Considering Limited Input Data

A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamics (biomass and snow cover), soil pore volume, soil texture and water content over the soil column.

Carbon uptake and water vapor exchange in a pasture site in the Brazilian Cerrado

As Brazil becomes one of the largest agricultural and livestock producers, the expansion of cultivated pasture and crop areas in the Cerrado region is expected to cause significant changes in carbon and water vapor fluxes. While this change in land use promotes modifications in regional carbon uptake and water cycle, eddy covariance studies carried out in pasture areas in the Brazilian Cerrado remain scarce. The present study aimed to quantify the carbon uptake and water vapor exchange of these areas in the Brazilian Cerrado. The experimental site was installed in an area covered by Brachiaria sp. in Três Lagoas – MS, Brazil. Data were collected during two hydrological years (2016–2017 and 2017–2018). Pasture strongly acted as a carbon sink; its total accumulation reached −208.9 (±9.6) and −171.8 (±15.8) g C m−2 in the first and second hydrological years, respectively. Average evapotranspiration of 2.0 (±1.1) mm d−1 was observed in the two hydrological years. There was significant reduction in evapotranspiration at the pasture site during the driest months of the year (June and July), mainly in the second hydrological year, which recorded an average of 0.4 (±0.1) mm d−1. The lower recorded water availability in the soil led to the release of 86.7 g C m−2 (over 85 days) in the dry season of the second hydrological year. Ecosystem-level water use efficiency observed during the higher soil water deficit facilitated the survival of the grass. This information is the basis for further studies about the impact of climate change on carbon uptake and water vapor flux by cultivated and natural pasture; additionally, the present research will allow comparisons of carbon storage results to other ecosystems and to support studies of agrometeorological modeling.

Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields

Agricultural drought has great impact on crop yields. Soil water content can directly reflect the degree of agricultural drought. The Water Cloud Model (WCM) is a vegetation contribution model which has been widely used in soil water content retrieval over vegetated areas. However, when optical remote sensing images are absent or are of a poor quality due to cloud cover and haze, the WCM parameters, describing the characteristics of vegetation layer, are hard to obtain. In order to effectively retrieve the soil water content over wheat fields, an agro-meteorological model, Simple Algorithm For Yield estimate (SAFY) model, was introduced to simulate the daily values of the WCM parameters during the whole winter wheat growth stages. Firstly, the leaf area index (LAI), retrieved from two Sentinel-2 (high-resolution optical) satellite images, was used to calibrate the parameters of the SAFY model in the Guanzhong Plain, PR China. The calibrated SAFY model was applied to simulate daily LAI. Then, Sentinel-1 backscatter coefficient, average ground measured soil water content from a depth of 0–12 cm and the simulated LAI were used to calibrate the WCM parameters to retrieve soil water content. In a final step, the soil water content retrieval results were validated at rain-fed and irrigated sampling sites, respectively. The SAFY model simulates the LAI of the Sentinel-1 sensing dates and uses it as an accurate vegetation description parameter for the WCM, which effectively solves the problem that the vegetation description parameter in the WCM cannot accurately describe the surface vegetation state at the time of radar satellite passing territory due to the Sentinel-1 and Sentinel-2 data in the study area cannot be acquired on the same day, and thus improving the accuracy of the WCM soil water content retrieval. For all sampling sites, the soil water content retrieval results have a satisfactory overall accuracy, and the soil water content retrieval results from rain-fed sampling sites have better accuracy than those from irrigated sampling sites. The soil water content retrieval results at the harvesting stage of winter wheat have the best accuracy among the three field experiments (R2 = 0.66; RMSE = 2.24%), followed by the tillering stage (R2 = 0.56; RMSE = 2.56%) and jointing stage (R2 = 0.36; RMSE = 2.74%). These results indicated that the combination of SAFY model and WCM can effectively retrieve the soil water content at different growth stages of winter wheat in the absence of optical remotely sensed images, and can provide significant benefit for soil water content retrieval over agricultural fields at the regional scale.

Evaluation of alternative methods to calculate evapotranspiration and their impact on soybean yield estimation

Despite the several studies comparing methods for evapotranspiration (ETo)estimation, scientific reports demonstrating their use and evaluation when coupled to agrometeorological models have not been analyzed, particularly in regard to the comparison of the estimated results obtained in the modeling with the real production data experimentally obtained in the field. The present study evaluated nine alternative methods to calculate ETo for estimating soybean yield, associated with actual yields obtained in irrigated and non-irrigated fields, at three sowing periods during the 2013/14 crop season in Southern Brazil. All methods were evaluated in relation to the standard Penman–Monteith method. Their performance was measured through regression analysis and statistical coefficients submitted to the Tukey test. ETo values obtained through the alternative methods were used to calculate water balances for soybean, considering irrigated and non-irrigated environments. Theoretical and real potential yields were higher in later sowings. The Priestley–Taylor method was the best to estimate daily ETo alternatively to that recommended by FAO (Penman–Monteith). On the other hand, the alternative method of Thornthwaite–Camargo was the best for estimations at 10-day periods, in all sowing dates. Furthermore, the methods of Thornthwaite–Camargo, Benevides–Lopez, Camargo, and Thornthwaite showed the smallest deviations to estimate ETo (10-day periods) for calculating actual yields (Ya).

Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Economic and agrometeorological modeling of sugarcane productivity in São Paulo state, Brazil

AbstractSugarcane (Saccharum officinarum L.) productivity is dependent on climatic and economic conditions. The goal of this research is to combine economic and agrometeorological modeling on sugarcane productivity in the 18 important sugarcane production microregions of the São Paulo state in Brazil from 1995 to 2012. Cluster analysis was used to identify two groups of microregions with similar sugarcane actual productivity (PR) within each group. Group 1 presented higher values compared to group 2 in all years. However, the sugarcane attainable productivity (PA), estimated by the agrometeorological model, of group 2 was higher than group 1 in 17 of the 18 years analyzed. Finally, a statistical model was used to quantify the influence of sugarcane price, rural credit concession and PA on PR in all microregions and in the two groups. For all microregions, sugarcane price was the most important explanatory variable for PR, followed by PA and the rural credit concession in third. PR of Group 1 was mainly affected by sugarcane prices and PR of group 2 was mainly affected by PA. Therefore, the group 1 sugarcane producers were able to respond to price changes regardless of PA in this group. This can occur due to a better management, bigger investments or smaller metereological adversity of group 1. In conclusion, the agrometereological (PA) and the economic (sugarcane price and rural credit concession) variables affected the PR in the analyzed regions. However, crop pathogens, meteorological adversities or human management have led to this situation (limiting and reducing factors).

GROUNDWATER MANAGEMENT FOR IRRIGATED AGRICULTURE IN SOUTH-EASTERN ALGERIA BY APPROACHING CROP WATER NEEDS

The demand for irrigation is growing and will continue to grow in the future due to increasing food demand and climate change. Data on current and future demands are essential for better management and planning of water resources use. The province of Biskra is ranked as the largest consumer of irrigation water in the cities of South East Algeria with 42% of annual consumption. Under the arid climatic conditions of this region, it is inevitable to irrigate and the majority of irrigation water comes from groundwater. The objective of our study is to evaluate the irrigation water withdrawals in the region using the agro-meteorological model CROPWAT for the 2014-2015 campaign over the entire province, and to compare them with the official data of the Directorate of Water Resources Services (DRE) and the Sahara River Basin Agency (ABHS). Our results were superior to their data and the water consumption per hectare of water was also superior to the results of the SASS (2015) but similar to the Döll (2010). With increasing withdrawals and a decrease in recharge, we could fear economic and ecological problems in a region under severe water stress.

Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019

The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December.

A SPECTRAL AGROMETEOROLOGICAL MODEL FOR ESTIMATING SOYBEAN GRAIN PRODUCTIVITY IN MATO GROSSO, BRAZIL

ABSTRACT This study used spectral data integrated with the agrometeorological model by Doorenbos and Kassam to estimate soybean grain productivity in the state of Mato Grosso, Brazil. In the developed model, spectral data were used instead of meteorological data and biophysical parameters of the crop. For this purpose, the products of real and potential evapotranspiration (MOD16), normalized difference vegetation index – NDVI (MOD13Q1), and leaf area index (MOD15A2H) from the MODIS satellite were used, in addition to sunstroke data obtained by using the visible channel from the satellite GOES IMAGER. The results obtained showed that, with the proposed methodology, it was possible to follow the development of soybean cultivation throughout the cycle and to estimate production and productivity in the study area. Willmott's agreement index was 0.99 and 0.96 and Pearson's correlation coefficient was 0.99 and 0.84 for production and productivity, respectively.