Yield Variability Research Articles

Modelling maize yield impacts of improved water and fertilizer management in southern Africa using cropping system model coupled to an agro-hydrological model at field and catchment scale

AbstractThis study quantifies the effect of fertilizer and irrigation management on water use efficiency (WUE), crop growth and crop yield in sub-humid to semi-arid conditions of Limpopo Province, South Africa. An approach of coupling a cropping system model (DSSAT) with an agro-hydrological model (SWAT) was developed and applied to simulate crop yield at the field and catchment scale. Simulation results indicated that the application of fertilizer has a greater positive effect on maize yield than irrigation. WUE ranged from 0.10–0.57 kg/m3 (rainfed) to 0.84–1.39 kg/m3 (irrigated) and was positively correlated with fertilizer application rate. The combined application of the variants with deficit irrigation and fertilizer rate (120:60 kg N:P/ha) for maize turned out to be the best option, giving the highest WUE and increasing average yield by up to 5.7 t/ha compared to no fertilization and rainfed cultivation (1.3 t/ha). The simulated results at the catchment scale showed the considerable spatial variability of maize yield across agricultural fields with different soils, slopes and climate conditions. The average annual simulated maize yield across the catchment corresponding to the highest WUE ranged from 4.0 to 7.0 t/ha. The yield gaps ranged from 3.0 to 6.0 t/ha under deficit irrigation combined with 120N:60P kg/ha and ranged from 0.2 to 1.5 t/ha when only applying deficit irrigation but no fertilizer. This information can support regional decision makers to find appropriate interventions that aim at improving crop yield and WUE for catchments/regions.

Zonal Application of Plant Growth Regulator in Cotton to Reduce Variability and Increase Yield in a Highly Variable Field

Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive growth and provide suitable plant height for harvest operations. This study evaluates the effect of variable-rate PGR application compared to constant-rate application to reduce yield spatial variability and increase yield. The variable-rate approach was carried out in 2020 based on zonal applications defined by clustering analysis using soil electrical conductivity, vegetation indexes, and yield maps. Application doses and timings were determined by integrating plant height measurements for the whole field in 2019 and by zone in 2020. To compare the two procedures, cultivar and plant populations were kept constant; fertilization and accumulated rain were similar in both seasons. A reduction in yield spatial variability due to the zonal application was observed, with yield coefficient of variation (CV) decreasing from 18% in 2019 to 12% in 2020. Spatial and temporal analysis of Normalized Difference Vegetation Index satellite images showed higher CV values in 2019 (constant-rate) reaching 30% at the end of the season, whereas in 2020 (variable-rate) CV was constant (approximately 10%). Cotton yield increased from 3.5 to 4.3 t ha-1 between 2019 and 2020, which can be partially attributed to the variable-rate approach. The variable-rate approach based on application zones and plant height measurements was a viable strategy for reducing yield spatial variability and likely increasing yield in a highly variable cotton field.

A Numerical Evaluation of the Impact of Operational Ground-Based Glaciogenic Cloud Seeding on Precipitation over the Wind River Range, Wyoming

Abstract This study evaluates an operational glaciogenic cloud-seeding program using ground-based generators of silver iodide (AgI), with a total of 190 seeded storms over 10 cold seasons, using the Weather Research and Forecasting Weather Modification (WRF-WxMod) scheme at 900-m grid spacing. This study examines both the quantitative change in precipitation and the ambient and cloud conditions impacting seeding efficacy. An ensemble approach is used, with differing model boundary conditions, ice nucleation physics, concentrations of cloud condensation nuclei, and boundary layer schemes. This is intended to provide an envelope of uncertainty of natural clouds and seeding impacts. The simulations are validated against radiosonde, snow gauge, and microwave radiometer observations, and the seeding impact is inferred from simulations with/without AgI seeding. The seeding-induced precipitation enhancement (“yield”) varies greatly between storms. A small portion of the cases produces the majority of the yield. Overall, the precipitation in the target area (the Wind River Range in Wyoming) increased by 1.10% ± 0.13% in the 10 years of operational seeding. This rather low fractional increase is related to the frequent seeding at unsuitable times, primarily because of low-level flow blocking. The flow and cloud structure for select cases are examined to provide better insight into the variability of yield. Cases with unblocked surface flow and abundant cloud liquid water tend to be the most productive. The technique presented here can be readily adapted to evaluate the seeding impact of other long-term glaciogenic seeding operations and to improve their operational efficiency. Significance Statement In the United States and elsewhere, there are several operational programs to enhance cold-season precipitation through glaciogenic seeding of orographic clouds. The impact of such activity on seasonal precipitation has always been difficult to quantify. Recent observational and numerical modeling studies indicate that orographic cloud seeding can increase precipitation, although the amounts and optimal seeding conditions remain uncertain. Operators lack guidance about the seeding efficacy and about the most suitable environmental conditions. In recent years a model parameterization, called Weather Research and Forecasting Weather Modification (WRF-WxMod), has been tested against detailed measurements. This sets the stage for our work, a well-designed numerical evaluation of 10 years of operational cloud seeding over the Wind River Range, a mountain range in Wyoming that feeds the Colorado River basin and other watersheds. The WRF-WxMod based simulation experiment presented here, one of the most computationally expensive numerical experiments on this subject to date, quantifies seeding impact and its uncertainty. It is demonstrated with a high degree of confidence that over this 10-yr period, suitable seeding conditions were rare over this mountain range, that most seeding events were unproductive, and that, as a result, the overall yield over 10 years was a mere 1.1%.

Increasing interannual climate variability during crop flowering in Europe

Climate change has increasingly adverse effects on global crop yields through the occurrence of heat waves, water stress, and other weather-related extremes. Besides losses of average yields, a decrease in yield stability—i.e. an increase in variability of yields from year to year—poses economic risks and threatens food security. Here we investigate a number of climate indices related to adverse weather events during the flowering of wheat, maize and rapeseed, in the current cultivation areas as well as the main European producer countries. In 52 projections from regional climate models, we identify robust increases in the interannual variability of temperature, precipitation and soil moisture by ∼+20% in standard deviation in the model median. We find that winter wheat is most exposed to variability increases, whereas rapeseed flowering escapes the largest increases due to the early flowering time and the northern locations of cultivation areas, while the opposite (escape due to southern locations and late flowering) is true for maize to some extent. Considering the timing of crop development stages, we also find a robust increase in the variability of the temporal occurrence of flowering, which suggests a decreased reliability in the timing of crop stages, hampering management steps like fertilization, irrigation or harvesting. Our study raises concerns for European crop yield stability in a warmer climate and highlights the need for risk diversification strategies in agricultural adaptation.

Copernicus global crop productivity indicators: An evaluation based on regionally reported yields

Climate variability places significant pressure on climate-sensitive sectors such as agriculture. Information on crop-related climate and water indicators under climate change becomes essential. The Copernicus Climate Change Service (C3S) provides reliable information about climate change based on climate models, Earth Observations, and in situ data to support adaptation policies. Within this C3S context, the Global Agriculture Sectoral Information System (SIS) has developed a new product based on global crop productivity indicators to document the effects of climate variability on crop yield variability. This study focuses on the evaluation of climate-enhanced, earth observation (EO) based crop productivity indicators i.e., Total Weight Storage Organs (TWSO, equivalent to yield), Total Above Ground Production (TAGP). These variables provide insights into the Spatio-temporal variability of yield, productivity, and development of four major staple crops (rice, maize, soybean, and wheat) for their main production regions at the global scale for the period 2000–2018. In this study, the evaluation was carried out spatially and temporally for the United States of America, India, and China for the period 2000–2018, using reported yield statistics aggregated to the lowest available administrative level of each region. For almost all the crops in the three countries, the skilled error can be reduced by more than 25% for both the indicators TAGP and TWSO compared to the trend of reported yield, suggesting considerable performance in assessing interannual yield variability. Results indicated both indicators individually performed well in many important producing areas of the USA, India, and China. However, if we compare both the indicator’s performance in terms of production totals in the USA, India, and China, TAGP contributes ∼27–50% of total crop production in the USA and India, and ∼47–75% in China. While, TWSO contributes ∼24–41% in the USA and India, and 5–65% in China.

Assessment of the relations between crop yield variability and the onset and intensity of the West African Monsoon

Assessment of the relations between crop yield variability and the onset and intensity of the West African Monsoon

Assessment of ethnobotanical uses, household, and regional genetic diversity of aroid species grown in northeastern India.

Aroids are an important group of indigenous tuber crops, grown widely for their leaves, petioles, stolons, corms, and cormels. A total of 53 genotypes were evaluated for their genetic diversity in northeastern region of India. At household level, a total of 16 landraces of Aroids were recorded having different ethnobotanical uses. Based on the population study under Jhum/Shifting farming, landrace Rengama was dominant in area with 47% of the total population followed by Tamachongkham and Tasakrek. However, Pugarkusu and Chigi occupied 33.0 and 24.0% of the population, respectively under backyard farming, and were considered as major landraces. Tamachongkham, high in acridity and total oxalate content (0.82%), was used for cooking with meat, while Tasakrek was used as a baby food due to high total sugar (>3.0%), low in acridity, and total oxalate content (<0.12%). The Simpson's diversity index of the backyards was higher (0.80) as compared to Jhum field (0.63). The genotypes showed wider variability in growth and yield attributes like; plant height (89.4-206.1 cm), number of side shoots (1.84-5.92), corm weight (38.0-683.3 g), cormel weight (14.0-348.3 g), yield (0.24-1.83 kg plant-1). Similarly, wide variations were also observed for quality traits like total sugar (1.93-4.94%); starch (15.32-32.49%), total oxalate (0.10-0.82%), and dry matter (16.75-27.08%) content. Except for total oxalate, all the growth and yield attributes have shown high heritability and moderate to high genetic advance. Molecular analysis (33 polymorphic SSR markers) detected a total of 136 alleles, ranged 3 to 8 alleles per marker. The observed heterozygosity (0.24) was less than expected heterozygosity (0.69). The group-wise maximum genetic divergence was observed between Colocasia fallax (cv. Chigi) to C. esculenta var. aquatilis (cv. Tharsing); C. fallax (cv. Chigi) to C. gigantea (cv. Ganima) and C. gigantea (cv. Ganima) to Xanthosoma spp., while it was least between eddo and dasheen. The findings indicated, a wider diversity and distinct ethnobotanical uses of Aroid landraces at the house hold levels, which should be conserved and popularized to ensure nutritional security.

A review of pasture yields and growth rates in Northland

ABSTRACT Pastoral grazing is the major land use in Northland, New Zealand. This study analysed the distribution and variability of pasture dry matter (DM) yields across four distinct physiographic regions, using published and original unpublished datasets collected from 1958–2021. Dargaville had the highest mean annual pasture yield of 11900 kg DM/ha, Kaitaia 9970 kg DM/ha and Kaikohe and Whangarei at ∼8400 kgDM/ha. Pasture water use (DM yield/mm rainfall) ranged from 10.8 kg DM/mm/ha (Kaikohe) to 16.4 kg DM/mm/ha (Dargaville). Kaitaia showed the largest annual yield variability (>2300 kg DM/ha). The relationship between pasture growth and temperature was quantified to estimate temperature adjusted growth rates for these regions. Whangarei spring growth was low at 3.19 kg DM/°Cd before it declined after 2560°Cd, or ∼7630 kg DM/ha, to 1.85 kg DM/°Cd, due to summer dry conditions. These results suggest pasture production was low compared with other regions of New Zealand. The low production and persistence may be compounded by the lack of readily available supplementary feed during summer dry or winter wet conditions. This increases the risk of overgrazing which reduces plant reserves and total light interception which results in physical damage to pasture plants and lower than potential yields.

Understanding Sentinel-1 backscatter response to sugarcane yield variability and waterlogging

Sentinel-1 observes the whole globe every 12 days (6 days when both satellites were operational) and provides a wealth of data relevant to agriculture. Sugarcane cultivators could potentially benefit from these data by using them to assist operational and management practices. However, first, thorough understanding is needed of Sentinel-1 backscatter and its behavior over sugarcane canopies. In this study, we aimed to improve understanding of how Sentinel-1 backscatter responds to sugarcane yield variability and waterlogging. In order to do so we focused on an irrigated sugarcane plantation in Xinavane, Mozambique. In the analysis presented, we assessed different polarizations, their ratio, and benchmarked them against optical indices and passive microwave observations in different seasons. With the help of a large sugarcane yield dataset, we analyzed how backscatter relates to sucrose yield variability in different seasons. We found VV backscatter related to the stalk development, the most important reservoir for sucrose accumulation. In addition, in a season with reported waterlogging, optical and radar observations showed a delay in sugarcane crop development. Further analysis showed the presence of water underneath the canopy caused an increase in all polarizations and the cross ratio (CR). The results imply that Sentinel-1 backscatter contains information on both waterlogging under the canopy as well as sucrose development in the stalk. By isolating and quantifying the impact of waterlogging on backscatter, it will be possible to further quantify sucrose development with backscatter observations and identify waterlogging simultaneously.

Growth, yield productivity, and oil composition of two Amaranthus species grown under two different environmental conditions in Egypt

AbstractAmaranthus plants are very rich in nutritional benefits with significant traditional uses especially against inflammations. Herein, the growth productivity, oil quality, and compositions of the two promising novel food resources, Amaranthus caudatus and Amaranthus tricolor, were evaluated in two different locations in Egypt (El‐Sharkia and El‐Minia) which have different geographical regions. Analyses of geographic positions, climatic data, and soil characteristics of the two locations were evaluated. Plant heights, number of branches/plant, seed yield, and inflorescence length of the two plant species were studied in both locations. Additionally, the seed oil yields along with their gas chromatography–mass spectroscopy (GC–MS) profiles were characterized. The findings revealed a significant difference between the two locations, with a strong correlation between the yields and the climate conditions. The two plant species cultivated in El‐Minia showed significant records of overall growth indicators than El‐Sharkia. A worthy variability in the oil yields and composition of El‐Minia samples exhibited higher contents of dry matter in both plants (89.01 and 89.03 g/100 g dry tissues, respectively) than those of El‐Sharkia. Amaranthus caudatus cultivated in El‐Sharkia showed higher lipids components (7.02 g/100 g dry seeds) and saturated (19.13 g/100 g dry seeds) and unsaturated fatty acids (61.32 g/100 g) than those of El‐Minia site. In addition, A. tricolor cultivated in El‐Sharkia contained higher lipids contents (6.87 g/100 g) and unsaturated fatty acids (61.75 g/100 g dry seeds) than El‐Minia with the supremacy of El‐Minia samples in saturated fatty acids (19.03 g/100 g dry seeds). A strong correlation between the lipids and fatty acids of A. caudatus and A. tricolor was determined, and the cultivation locations and conditions were found based upon the GC–MS analysis as well as statistical significance.

Spatial Pattern of Cotton Yield Variability and Its Response to Climate Change in Cotton Belt of Pakistan

Spatial Pattern of Cotton Yield Variability and Its Response to Climate Change in Cotton Belt of Pakistan

Genotypic and environmental contributions to lint yield, yield components, and fiber quality in upland cotton from Arkansas variety trials over a 19‐year period

AbstractLint yields and their underlying yield components can be substantially influenced by genotype and environment. Thus, the objectives of the current study were to quantify genotypic and environmental contributions to lint yield, yield components, and fiber quality in multi‐site variety trials conducted in Arkansas over a 19‐year period, to identify the traits most strongly associated with lint yield, and to quantify long‐term trends in the aforementioned traits. Annual assessments included lint yield, seeds per hectare (SPH), intra‐boll yield components, and fiber quality parameters. Production environment accounted for a higher percentage of all yield variability in 15 out of 19 years. However, four years were identified in which genotypic contributions were greater than environmental contributions. Genotype was a dominant driver of variability in lint percent, lint index, seed score, seed index, fibers per seed, fiber density, fiber length, strength, and uniformity. Production environment was the dominant driver of variability in SPH, and micronaire. Correlations between yield components and yield varied substantially from year to year, even when genotype was the primary yield driver. A regression function that utilized lint index and SPH was a stronger and more consistent indicator of genotypic variation in lint yield than any single yield component. Finally, long‐term trends show that lint percent has increased significantly over the past 19 years, whereas seed index has declined 19.6% for cotton varieties with the highest lint yields.

Development and the Effect of Weather and Mineral Fertilization on Grain Yield and Stability of Winter Wheat following Alfalfa—Analysis of Long-Term Field Trial

Within the framework of a long-term experiment, established in 1955, we evaluated the development and effects of weather and mineral fertilization (Control, NPK1, NPK2, NPK3, NPK4) on the yield and stability of winter wheat following alfalfa. In total, 19 seasons were analysed. The weather changed considerably at the experimental site. Significant increases in minimal, mean, and maximal temperatures were dated to the period 1987-1988, while precipitation remained the same to this day (insignificantly increasing trend by 0.5 mm per annum). Rising temperatures in November, May, and July positively affected wheat grain yield, especially in treatments with higher N doses. No relationship between yield and precipitation was recorded. Highest inter-annual yield variability was recorded in Control and NPK4 treatments. Although minerally fertilized treatments provided slightly higher yields, the difference between Control and NPK treatments was insignificant. According to the linear-plateau response model, the recommended dose of 44 kg ha-1 N corresponds with yield of 7.4 t ha-1, while Control provides an average yield of 6.8 t ha-1. The application of higher doses did not lead to significant grain yield increase. Alfalfa as a preceding crop reduces the need of N fertilization and contributes to sustainable conventional agriculture, however, its share in crop rotations is decreasing both in the Czech Republic and in Europe.

Climate change effects on Chickpea yield and its variability in Andhra Pradesh, India

Farmers usually do not know the precise output that is affected by climatic factors such as temperature and rainfall and are characterized by inter-annual variability, part of which is caused by global climate change. No study covers the influences of climate factors on yield and yield risk in the context of chickpea farming in Andhra Pradesh, India. In this context, this study aimed to investigate the trends in climate change variables during Rabi season (October to January, 1996-2020) and evaluated their variability on chickpea yields across different agro-climatic zones in Andhra Pradesh by employing Just and Pope production function. Four non-parametric methods-Alexandersson’s Standard Normal Homogeneity Test, Buishand’s Range Test, Pettitt’s Test and Von Neumann’s Ratio Test are applied to detect homogeneity in the data. Mann–Kendall (MK) test and Sen’s slope (SS) method were employed to analyze monthly rainfall trends and minimum and maximum temperature trends. Results of Just and Pope (panel data) quadratic and Cobb-Douglas methods revealed that monthly minimum temperature positively influenced the mean yield of chickpea (0.22% and 0.16%, respectively). However, rainfall (-0.41% and -0.31%) and maximum temperature (-0.08% and -0.04%) negatively influenced the mean yield of chickpea under quadratic and Cobb-Douglas models, respectively. Accordingly, rainfall (0.08% and 0.06%) and maximum temperature (0.83% and 0.72%) positively influenced the yield variability and minimum temperature (-0.77% and -0.67%) reduced yield variability of chickpea under quadratic and Cobb-Douglas models respectively. In view of these findings, it is imperative to advocate the farmers about the importance of cultivating drought-tolerant chickpea varieties, drought-proofing and mitigation strategies, micro-irrigation practices and improving their access to agro-meteorological information towards sustainable chickpea cultivation in Andhra Pradesh.

Climate stressors modulate interannual olive yield at province level in Italy: A composite index approach to support crop management

AbstractAlthough a large part of Italy is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable interannual yields with associated agronomic and management costs. A detailed overview of major climate stressors and their ongoing impacts on olive yield variability at a broad spatial–temporal scale across Italy could improve the understanding of how interannual olive yields are modulated by seasonal local climate and would enhance the development of actionable services to alert stakeholders of potential climate risks. We analysed aggregated olive yield data from the Italian National Statistics Institute (ISTAT) at the provincial level during 2006–2020, and several climatic variables from the Reanalysis v5 (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) as a basis to (i) explore olive yield trends and inter‐annual variations over the whole country; (ii) identify major seasonal climate stressors likely responsible for the largest variations in yield and (iii) develop a composite index that summarizes the risk of having exceptionally low yields due to the co‐occurrence of multiple stressors. To this end, we defined two extreme classes of yield, namely, exceptionally low and high yields (LY and HY, respectively) and explored the climatic variables aggregated on a bimonthly time scale that influenced yield outcomes. Our analysis showed that exceptional low yields have been erratically increasing since 2014 with temperature‐related variables having the highest impact, especially a relatively warm winter and cool early autumn. These period‐specific variables were major components of the resulting composite index predicting the likelihood of LY ranging from 28% to 49% due to increasing stress effects. Possible explanations of our findings are discussed, including the proliferation of the olive fly. We suggest the composite risk approach could lay the groundwork for an integrated meteorological seasonal forecasting system that provides timely insights on factors affecting within‐season olive yield development.

Genotype Selection for Grain Yield of Sorghum through Generalized Linear Mixed Model

The classical model only provides a correct analysis if all the effects are fixed. For experiments that include fixed and random effects, the general linear mixed model is appropriate for handling the non-normal distributed response variables. The aim of this study is to perform the genotype selection through a generalized linear mixed model and identify the impact of treatment and the related traits on grain yield. The data were collected using a lattice square design and measured the phenotype traits of sorghum. The result of PCA was used as an input variable for the general linear mixed model. The data analysis was performed using a general linear mixed model with maximum likelihood methods to estimate the parameters of the model. The result showed that the grain yield had a gamma distribution and a treatment effect on grain yield. The first principal component was significant for grain yield. The variability of grain yield due to the random effects of replication within treatment, genotype, and the interaction of genotype by treatment were significant. The best genotypes effective for the mass production of sorghum were G137, G66 and G156 under stress conditions and G55, G41 and G78 under irrigated conditions. Overall, genotype selection using a general linear mixed model for grain yield is recommended for genotype selection of plant breeding.

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.

Genetic Variability, Heritability and Genetic Advance of Yield and Yield Contributing Characters in Putative M2 Dolichos Bean (Lablab purpureus L.) Accessions

Dolichos bean (Lablab purpureus L.) is a multipurpose, orphan leguminous crop in Kenya .It is characterized with narrow genetic diversity and low yield output. Use of nuclear techniques such as gamma ray induction in breeding for improved genetic variability is an effective approach in improvement of this important crop. Studies on genetic variability estimates of two Kenyan dolichos bean accessions were undertaken as the pre-requisites for selection of potential high yielding putative accessions for production. This study was conducted between May 2020 and October 2020 at biotechnology department, University of Eldoret research field, Kenya. 0gy, 300gy and 400gy mutant accessions of Eldo maridadi and Eldo black I respectively were raised in Randomized Complete Block Design (RCBD) with three replications. Data was collected either on plot basis or from randomly taken 3 plants on days to 50% flowering, leaf length, number of flower buds per race , raceme length (cm) , plant height (cm), number of nodes per raceme , pod length (cm), 100 seed weight (g), dry seed yield (g). The data collected for each quantitative trait was subjected to analysis of variance (ANOVA) for simple lattice design. There was high PCV and GCV coupled with high heritability in, raceme length (cm), plant height (cm), number of nodes per raceme, pod length (cm), 100 seed weight (g), dry seed yield per plant (g) in Eldo black I accession traits than in Eldo maridadi .The different accession following mutation did exhibit difference in trend of genetic variability estimates. An indication of genotypic differences, response to mutation, lesser influence of environment in expression of the traits, prevalence of additive gene action in their inheritance and amenability of the traits to simple selection. Eldo maridadi accessions exhibited high to low heritability with low genotypic advance as percent of mean in evaluated traits thus direct selection on this traits may not beneficial. Other traits or further improvements on yield potentiality and yield traits on the accessions of this genotype are suggested by creating variation and selection.

Within-field yield stability and gross margin variations across corn fields and implications for precision conservation

Soil spatial variability is a primary contributor to within-field yield variation across farms. Spatio-temporal yield stability and variability can be assessed through multi-year yield monitor data and geostatistical techniques. Our objective was to delineate yield stability zones using multi-year yield data coupled with gross margins to plan precision conservation prescriptions. This study employed corn yield measurements from 2018, 2019, 2020 and farm economics data to compile yield stability and gross margin maps for nine Texas Blackland Prairie corn fields, and identified nonprofitable areas in each field that may be unsuitable for crop production. Yield stability zones were delineated using mean and coefficient of variation of multi-year yield maps (Zone A: high yield, stable; Zone B: high yield, unstable; Zone C: low yield, unstable; and Zone D: low yield, stable). Approximately 57% of the area in the fields was classified as unstable and, nearly 29% of the area yielded consistently below the field mean (Zone D). Gross margin for stability zones ranged from − $693 to $775/ha. Stability zones A and B generally had positive gross margins, whereas zones C and D had negative margins. Based on yield and gross margin assessment, yield stability zone D could be removed from row crop production. As a part of the Long-Term Agroecosystem Research Network Common Cropland Experiment, Zone D was removed from production (fields Y-8 and Y-13) or received reduced inputs (field SW-16 and W-13). Further study is needed to verify the farm-level economic benefits to producers and to evaluate the environmental benefits of precision conservation.

Effect of edapho-climatic conditions on fruit productivity, yield and olive oil quality in olive orchards (Olea europaea L.): case of a semi-arid region

The objective of the present work is to evaluate the relationship between the edapho-climatic conditions of the cultivation environment and the variability of olive productivity, yield, and quality of olive oil in orchards of the Moroccan Picholine (PM) population variety (Olea europaea L.). This work was carried out on different agricultural territorial units (agroecosystems) of the Beni Mellal-Khenifra region with a semi-arid climate during four agricultural seasons (2016/2017 to 2019/2020). For this purpose, the relevant agronomic and agro-biochemical parameters were studied, namely the stage of maturity, fruit and olive oil yields and quality indices. The data treatments of the results were performed according to descriptive and affiliated statistics by analysis of variance (ANOVA). We have found significant correlations (according to Pearson's R) between agroecosystems, particularly for weight of crude olives produced per hectare and temperature (R= 0.803), olive yield and humidity (R= 0.654), olive oil yield and temperature (R= 0.837), olive oil richness and humidity (R= -0.622), as well as the qualitative characteristics of the extracted oils, through the levels of free acidity and peroxide value (R = 0.467). These results confirm that in the studied semi-arid environments, the properties of the biotope of the cultivated olive groves, combined with the interannual agroclimatic variability, effectively influence the productivity of the olive trees, the oil production and the chemical and organoleptic quality of the extracted olive oil