Temporal Yield Variability Research Articles

Simulating water-limited potato yields across the Netherlands with (SWAP-)WOFOST: Experimentation, model improvement and evaluation

Water availability explains a large part of the spatial and temporal yield variability of ware potato in the Netherlands. Climate change is projected to lead to greater variability in water availability. Therefore, an accurate simulation of crop yield as a function of water availability is of high importance. Many crop models can simulate water-limited potato yields, but as detailed experimental data on drought and oxygen stress are scarce, models are often not well calibrated and evaluated. We set up a large experiment and an on-farm observational trial to calibrate and evaluate the simulation of water-limited yield of different potato cultivars across the Netherlands. In addition, we investigated the required model complexity to accurately simulate water-limited potato yields. The crop growth model WOFOST was used for simulations either as a single model or coupled with SWAP. Yields were simulated applying either only drought stress or by including both drought and oxygen stress. We employed two methods for simulating oxygen stress in SWAP-WOFOST. The calibration improved accuracy of simulated water-limited yields as demonstrated in our model evaluation. Both on sandy and clayey soils, water-limited yields were best simulated using SWAP-WOFOST while simulating oxygen stress using a process-based method in combination with a rooting density that decreases linearly with rooting depth. On sandy soils, the bottom boundary condition of free drainage was applicable. On clayey soils measured groundwater levels (or soil water pressure heads when measurements are not available) should be used as bottom boundary condition. On sandy soils both WOFOST and SWAP-WOFOST may be used, as results were similar. On clayey soils, WOFOST is less suitable, as it cannot simulate capillary rise. SWAP-WOFOST however has many options and parameters, which can result in large differences in results. Hence, a careful model set up and evaluation is required for each application.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

Large scale water yield assessment for sparsely monitored river basins: A case study for Afghanistan

This paper presents results from a study on water yield assessment across five major river basins of Afghanistan. The study was conducted using GR4J and GR4JSG precipitation-runoff models. The river basins were divided into 207 subcatchments and each subcatchment was divided into multiple functional units. The model was calibrated using observed streamflow data from 2008 to 2015 and validated over the 2016 to 2020 period. Model parameters were calibrated for an unregulated subcatchment in each basin and calibrated parameters from the best-performing subcatchment were transferred to other subcatchments. Results show that modelled water yield across the five basins varies from 0.3 mm in the Helmand basin to 248 mm in the Panj-Amu basin, with an average of 72.1 mm for the entire country. In the period of 2008 to 2020, area averaged water yield in the five basins varies from 36 to 174 mm. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) of surface water for the country. In addition, about 28.9 BCM generates annually in the neighbouring Tajikistan and Pakistan from snow and glaciers of the Hindu-Kush mountains. The elevated northern parts of Afghanistan, including parts of neighbouring Tajikistan are the primary water source. Water yield across the country varies between years but there is no consistent increasing or decreasing trends. About 60 to 70% of flow occurs between March to June. The study identified the high water yield areas and investigated variability at monthly, seasonal, and annual time scales. An importance finding is the large spatial and temporal variability of water yield across the basins. This information is crucial for long-term water resources planning and management for agricultural development.

Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel‐2 NDVI time-series images in an organic farming system

The study investigates the suitability of time series Sentinel-2 NDVI-derived maps for the subfield detection of a sunflower crop cultivated in an organic farming system. The aim was to understand the spatio-temporal behaviour of subfield areas identified by the K-means algorithm from NDVI maps obtained from satellite images and the ground yield data variability to increase the efficiency of delimiting management zones in an organic farming system.Experiments were conducted on a surface of 29 ha. NDVI time series derived from Sentinel-2 images and k-means algorithm for rapidly delineating the sunflower subfield areas were used. The crop achene yields in the whole field ranged from 1.3 to 3.77 t ha−1 with a significant within-field spatial variability. The cluster analysis of hand-sampled data showed three subfields with achene yield mean values of 3.54 t ha−1 (cluster 1), 2.98 t ha−1 (cluster 2), and 2.07 t ha−1 (Cluster 3). In the cluster analysis of NDVI data, the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability. The best period for identifying subfield areas starts from the inflorescences development stage to the development of the fruit stage. Analyzing the NDVI subfield areas and yield data, it was found that cluster 1 covers an area of 42.4% of the total surface and 50% of the total achene yield; cluster 2 covers 35% of both surface and yield. Instead, the surface of cluster 3 covers 22.2% of the total surface with 15% of achene yield. K-means algorithm derived from Sentinel-2 NDVI images delineates the sunflower subfield areas.Sentinel-2 images and k-means algorithms can improve an efficient assessment of subfield areas in sunflower crops. Identifying subfield areas can lead to site-specific long-term agronomic actions for improving the sustainable intensification of agriculture in the organic farming system.

Yield variability across spatial scales in high input farming: Data and farmers’ perceptions for potato crops in the Netherlands

Crop yields are determined by the biophysical environment and by farm management decisions, which in turn depend on socio-economic conditions of the farm(er). The interaction of these factors results in spatial and temporal yield variability. We assessed ware potato yield variability in the Netherlands across four agronomically relevant scales (among provinces, farms and fields and within fields) using five datasets with data on potato yield across space and time. Furthermore, we disseminated an online questionnaire among farmers to identify the perceived yield gap and the key yield gap explaining factors at farm level. Spatial yield variability was largest among fields, with a standard deviation of 8.5–11.1 t ha−1, and within fields, with a standard deviation of 7.7–8.7 t ha−1. Spatial yield variability decreased at higher aggregation levels, i.e., the standard deviation of among-farm yield variability was 4.0–6.1 t ha−1 and that of among-provinces 1.6–3.5 t ha−1. Mean yields of the datasets ranged from 46 to 52 t ha−1. Temporal yield variability explained 10–55 % of the total observed variation in crop yield and its magnitude was equal or larger than the spatial yield variability for almost all datasets. Farmers estimated the ware potato yield gap at 13–18 t ha−1, corresponding to 20–24 % of estimated yield potential, depending on the soil type and variety. Water deficit and water excess were considered the most important yield gap explaining biophysical factors. In addition, soil structure was an important biophysical factor on clay soils and diseases on sandy soils. Irrigation and fertilization were identified as the most important yield gap explaining management factors, whereas legislation and potato prices were identified as the key socio-economic factors influencing potato yields. However, the perceived yield gap explaining factors varied with soil type, variety and year. We conclude that reducing potato yield variability in the Netherlands can be achieved best at the field and within-field level, rather than at farm or regional level. When reducing yield variability is not feasible and/or desirable, inputs should be adapted to actual yield levels to achieve optimal environmental and economic sustainability.

Importance of incorporating spatial and temporal variability of biomass yield and quality in bioenergy supply chain

Biofuels made from biomass and waste residues will largely contribute to United States’ 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.

Ecological and evolutionary effects of crop diversity decrease yield variability

Abstract Higher plant species diversity decreases variability of plant community productivity. The stabilizing effect of plant diversity can result from species‐specific responses to environmental fluctuations and from shifts in competitive hierarchies. Evolutionary adaptation of species to surrounding plant diversity could further decrease productivity variability. We used a three‐year dataset from a crop diversity experiment with seven species to assess the effect of crop diversity and selection history on temporal variability of yield. We found contrasting patterns of temporal variability: Yield of species varied more in mixtures than in monocultures over years. However, at community‐level, we found lower yield variability in crop mixtures compared to monocultures, although only in combination with fertilizer application under Mediterranean climate. Furthermore, we found that a mixture selection history can increase yield productivity and decrease its variability, although only in monocultures. This suggests that the interspecific interactions among crops in mixtures act as an evolutionary selective force, promoting niche complementarity. Synthesis. Our results highlight the ecological and evolutionary role of plant interactions in crop mixtures, which can affect yield stability, while also reflecting on the importance of climate and resource availability in modifying the diversity‐stability relationship.

Burkina Rock Phosphate Fertilization Increases Nodulation and Yield of Cowpea under Zaï Cultivation in Sahelian Agro-ecosystem of Burkina Faso

Cowpea occupies a considerable place in the nutritional and economic balance of the rural population of Burkina Faso. However, its cultivation is marked by yield instability linked to soil depletion of nutrients, especially N and P, and irregular rains. The objective of this study was to evaluate the effect of phosphorus fertilization with the rock phosphate named BurkinaP, on the spatial and temporal variability of cowpea nodulation and yield. A multilocation test was conducted in 12 and 16 farmers’fields in 2013 and 2014, respectively, in 3 villages of 3 provinces of the northern region of Burkina Faso. Two treatments were compared: zaï without (ZS) and zaï with BurkinaP (ZP). Overall, dry weights of nodules and shoots at flowering stage, and grain at harvest, were significantly increased by BurkinaP. It is concluded that in soils where low availability of P limits crop yields of cowpea especially in arid sud-saharan areas of West Africa, the input of BurkinaP can improve cowpea N2-fixation, and increase and stabilize cowpea yields.

Analysis of Climate Variability and Its Impact on Crop Yield Over Waghemra Zone, Ethiopia

Climate variability manifested by seasonal rainfall and temperature variability greatly affects agricultural crop production under rain-fed conditions (main source of income for rural communities). Therefore, this study has focused on analyzing the impacts of climate variability on rain-fed wheat and sorghum production in Waghemra Zone, Ethiopia. Daily rainfall and temperature data were collected from National Meteorological Agency of Ethiopia and crop yield data was collected from Central Statics Agency of Ethiopia. Temporal variability of rainfall, temperature and crop yield was analyzed using coefficient of variation (CV) and standardized anomaly method. The rainfall and temperature trends were assessed via Mann-Kendall test and Sen’s slope estimator. The impacts of climate variability on crop yields were evaluated using correlation and regression analysis. The rainfall analysis exhibited large inter-annual and seasonal variation both in amount and distribution. The rainfall has showed decreasing trends in Belg and increasing in both annual and Kiremt season. Moreover, the detected trends are not significant. In contrast, minimum and maximum temperature showed significantly increasing trends at annual and seasonal time scales. The number of warm days and nights has increased and the number of cold days and nights has decreased. The regression analysis revealed that climate variability over the study area has differential effects on the yield of Wheat and Sorghum with coefficient of determination 86% and 84%, respectively. The observed rainfall and temperature variability has posed major risks to rain-fed agriculture and specific adaptation strategies are needed to cope with the risks, sustain farming and improve food security.

Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study

Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.

Machine learning in crop yield modelling: A powerful tool, but no surrogate for science

Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.

Diversifying bioenergy crops increases yield and yield stability by reducing weed abundance.

Relationships between species diversity, productivity, temporal stability of productivity, and plant invasion have been well documented in grasslands, and these relationships could translate to improved agricultural sustainability. However, few studies have explored these relationships in agricultural contexts where fertility and weeds are managed. Using 7 years of biomass yield and species composition data from 12 species mixture treatments varying in native species diversity, we found that species richness increased yield and interannual yield stability by reducing weed abundance. Stability was driven by yield as opposed to temporal variability of yield. Nitrogen fertilization increased yield but at the expense of yield stability. We show how relationships between diversity, species asynchrony, invasion, productivity, and stability observed in natural grasslands can extend into managed agricultural systems. Increasing bioenergy crop diversity can improve farmer economics via increased yield, reduced yield variability, and reduced inputs for weed control, thus promoting perennial vegetation on agricultural lands.

STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

Long-run Spatial and Temporal Yield Variability Analysis of Three Major Crops Affected by Fertilizer Use and Rainfall in Ethiopia over the Past 15 Years (2004/05–2018/19)

Ethiopia is one of the major producers of maize and wheat and the only producer of teff at a larger scale for grain in sub-Saharan Africa (SSA). Various efforts have been made by the government of Ethiopia to increase productivity over the past 15 years. Here we analyze a dataset with more than 1,260 yield observations from 2004/05 to 2018/19 for three crops (teff, maize, and wheat) in the 28 zones of the two major cereal-growing regions of the country. These two regions, Amhara, and Oromia represent around 81% of cropped area, 75% of fertilizer use, and 82% of cereal production of Ethiopia annually. Zonal level crop production data were used to analyze spatial and temporal patterns of teff, wheat, and maize yield. Zones were categorized as wet and dry based on annual rainfall. We dissect the evolution of yield trends over time and space, analyze yield variation, and evaluate whether growth of yields has increased, decreased, or stalled in recent years. We found that productivity of teff, wheat, and maize continued to increase from 0.95 to 1.76 metric tons per hectare (mt ha -1), 1.56 to 2.76 mt ha -1, and 1.72 to 4.0 mt ha -1 respectively, between 2004/05 and 2018/19. There was an average annual increase of 5%–8% during this time. The data also show a strong correlation of yield with rainfall and fertilizer use patterns; therefore, we recommend that the fertilizer advisory service should also make use of the rainfall conditions of the different locations to fine-tune fertilizer recommendations.

Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy

Farmer’s management decisions and environmental factors are the main drivers for field spatial and temporal yield variability. In this study, a 22 ha field cultivated with corn for more than ten years using different prescription maps of nitrogen application rates was investigated. Prescription maps were developed based on archived yield maps, soil analysis and recently integrated with Sentinel 2 satellite images. In addition, farmer experience and availability of variable rate application (VRA) requirements had an influence on the development of the homogeneous management zones. The initial approach with VRA was quite simple, based on a simple partitioning of the field into three rectangular zones (defined mainly based on previous yield maps and farmer experience). The partitioning changed with time and knowledge, evolving to the final five irregularly shaped zones (defined based on Farm works decision support software). Furthermore, since 2010 the farmer began using soil moisture sensor for irrigation decisions. Results of the present study highlight an improvement in corn yield and a reduction in total applied nitrogen. Corn yield improved on average by 31% on a ten years basis to reach more than 14 ton/ha dm. in 2018. At the beginning of VRA, yield maps showed a high spatial variation between field zones compared to reduced variation in the following seasons. In addition, the nitrogen applied reduced by around 23% while the total yield was improving. These results showed an increase in the partial factor productivity from less than 54 to around 87 kg of corn grain per kg of nitrogen applied. This promising result shows that farmer management decisions can improve every season by continuous monitoring of crop performance, understanding field variability and taking advantage of recently developed decision support software tools.

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

The anticipated increases in environmental variability associated with climate change may lead to enhanced abiotic plant stresses (e.g., heat stress, drought stress, etc.) resulting in greater inter-annual yield fluctuations and higher crop production risk. While there has been increasing attention to adaptation measures, there is little evidence available on how to change agronomic management strategies to maintain stable yields in winter rye production systems in Poland. This study uses rye yields from the unique Skierniewice Long-term experiment (Poland) to examine for the first-time the long-term effects of different nutrient regimes on crop yield stability from 1966 to 2015. Yields from six combinations of mineral fertilizers and lime (CaNPK, NPK, CaPK, CaNK, CaNP, Ca), with and without additional manure, were used to estimate the temporal yield variability of winter rye. A novel statistical approach based on a mixed model approach with REML (restricted maximum likelihood) stability parameter estimation was used. The results showed that the use of additional manure in ‘sub-optimal’ mineral fertilizer treatments, such as Ca and CaPK (without mineral N), reduced the temporal yield variability of rye. In contrast, additional organic input led to more variable rye yields in already ‘optimal’ treatments including mineral N (CaNPK and NPK), compared to those with no additional manure. Winter rye given CaNPK and NPK, without additional organic manure demonstrated high yield and low temporal yield variability. In contrast, yields of treatments with no mineral N (Ca and CaPK) and no additional manure supply were low and unstable. In addition, it was found that increasing soil organic carbon resulted in larger, more stable yields. These findings highlight the importance of ensuring rye crops receive sufficient fertilizer to maintain crop production levels and yield stability, especially in dry years. They also demonstrate the importance of avoiding the excessive use of organic manures when fertilizer inputs are sufficient to meet crop demand. Overall, the study provides novel insights about how to maintain grain yields and minimize temporal yield variation of rye in arable cropping systems, which will become increasingly important in a changing climate in Poland and in other temperate climate areas. This study also highlights the importance of soil organic carbon for improving the climate resilience of winter rye, while simultaneously meeting the demand for more sustainable management of the soil.

Yield variability trends of winter wheat and spring barley grown during 1932–2019 in the Askov Long-term Experiment

Abstract Designing cropping systems with low yield variability (or high yield stability) is becoming increasingly important because of the ongoing climatic and agronomic challenges. The trends in yield variability under various agronomic managements can be evaluated by conducting long-term experiments. By using a novel statistical analysis method, we estimated the long-term yield variability trends for winter wheat and spring barley grown in the Askov Long-term Experiment (Denmark) with different rates of mineral fertilizers (½, 1, and 1½ NPK) and animal manure (½, 1, and 1½ AM). Yield data from 1932 to 2019 were analyzed using a mixed model approach with restricted maximum likelihood (REML)-based parameter estimates. Across all nutrient treatments, winter wheat showed lower temporal yield variability than spring barley in the first decade. However, particularly, since 2006, the wheat yield variability trend increased mainly under treatments with higher NPK rates (1 and 1½). Spring barley also showed an increasing trend in yield variability; however, compared to wheat, this trend was less pronounced. Therefore, wheat yields were less stable than barley yields during the last decade. Wheat and barley yields fluctuated more under higher NPK rates (1 and 1½) than under a reduced rate (½ NPK). In general, animal manure provided more stable yields than NPK for wheat as well as for barley. The long-term trends in yield variability of cropping systems with winter wheat and spring barley were affected by the choice of crop, nutrient source and application rate, and increasing climatic variability.

Influence of Climate Variability on Soybean Yield in MATOPIBA, Brazil

The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.

A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand

Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

Management duration controls the synergistic effect of tillage, cover crop, and nitrogen rate on cotton yield and yield stability

Resilient agroecosystems are foundational for stable and profitable food, feed, and fiber production in the face of increasing climatic perturbations and environmental stresses. Enhanced soil and environmental benefits of cover crops applied to conservation tillage systems has been well documented. However, little is known about the timespan for interactions of no tillage and cover crops to achieve enhanced yield and yield stability while lowering N fertilization. Using a long-term continuous cotton experiment in southeastern USA, we analyzed yield data collected from 1986 to 2018 from 32 management systems to identify how management duration controls the synergistic effect of applied mineral N rates (0, 34, 67, and 101 kg ha−1), cover crops (no cover [NC], hairy vetch [HV], crimson clover [CC], and winter wheat [WW]), and tillage practices (no tillage [NT] and conventional tillage [CT]) on cotton yield and yield stability. Yield stability was analyzed using Finlay—Wilkinson regression model, Wricke’s Ecovalance, and coefficient of variation, and a mixed model approach was used to compare the yield and yield stability within three time phases (1−10 years, 11–20 years, and 21−33 years) at the 95% confidence level. During the initial 10 yr period (phase 1) CT resulted in greater cotton yield (7%) and yield stability than NT. However, in phase 2 (11–20 years) and phase 3 (21−33 years) NT led to greater yield (7%) and yield stability for almost all cover crop × N interactions, except for zero N following NC and WW. Increased N during the initial phase reduced both yield and yield stability under legume cover crops. During phases 2 and 3, however, the higher N rates (67 and 101 kg N ha−1) increased cotton yield, although management systems with 0 and 101 kg N ha−1 showed the largest temporal yield variability. Legume cover crops increased yield and yield stability under low N rates. The maximum combined yield and yield stability benefit was obtained from HV cover on NT with additional application of 34 kg N ha−1. Our results suggest that after the initial phase NT delivers the most consistent yield benefits while enhancing yield stability against unfavorable environmental conditions. Long-term integration of legume cover crops (particularly HV) to NT systems was effective in maintaining high yield and increasing yield stability while lowering N rates.