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Introduction: The study aims to evaluate the impact of climatological factors on rice yield and methane emissions in Southern Shaanxi’s rice cultivation areas, with the goal of informing effective Climate-Smart Agriculture (CSA) strategies. Methods: A three-year longitudinal analysis (2017-2019) was conducted, examining the correlation between rice productivity and weather conditions within the agricultural ecosystem. Data on rice yields and methane emissions were collected and analyzed to determine patterns and trends. Results: Significant correlations were identified between rice yield and weather conditions, with favorable weather for rice growth correlating with higher methane emissions. Methane emissions were particularly high during the vegetative and reproductive stages of rice growth, peaking 60 to 90 days after transplanting. Average emissions for this period were 245.2±80.1 kg CH4 ha-1 in 2017, 274.2±93.9 kg CH4 ha-1 in 2018, and 339.6±50.3 kg CH4 ha-1 in 2019. Total cumulative methane emissions over the entire rice cultivation period were 635.0±177.2 kg CH4 ha-1 in 2017, 661.2±239.2 kg CH4 ha-1 in 2018, and 679.4±205.4 kg CH4 ha-1 in 2019, with no statistically significant interannual differences. Discussion: The findings highlight the need to balance the goals of reducing greenhouse gas emissions for climate change mitigation with the enhancement of rice yield within CSA practices. The organic link between rice productivity and methane emissions under varying weather conditions suggests that an integrated approach to CSA is essential, considering climate adaptability, productivity, and greenhouse gas reduction. The study’s results contribute to a deeper scientific understanding of local agricultural ecosystems and provide a basis for developing management techniques for CSA. Conclusion: An integrated approach to CSA that takes into account the interplay between rice yield, methane emissions, and climatological factors is crucial for achieving sustainable agricultural practices in Southern Shaanxi. The study’s insights can guide the development of strategies that enhance both rice productivity and environmental sustainability [ABSTRACT FROM AUTHOR]

Quinoa (Chenopodium quinoa Wild.) is a drought and salinity-tolerant crop that originated in the Andes over 7000 years ago. It is adapted to different agroecological areas and can be grown from sea level to an altitude of 4000 m. The outstanding nutritional status of quinoa, with its high content of proteins, vitamins, and minerals, makes it a promising crop able to combat hunger and malnutrition in different countries in the 21st century. Quinoa cultivation has expanded from South America to Africa, Europe, Asia, and North America. Reviewing quinoa cropping practices will provide farmers with adequate recommendations for improving the agronomic and environmental sustainability of quinoa cultivation worldwide. For this reason, we conducted a systematic review of agronomic management practices in 148 field experiments conducted worldwide from 2000 to 2022. The collected data from the literature were analyzed and presented by location to determine high-performing genotypes, optimal planting dates, and other adequate cropping practices affecting quinoa performance and yield. Results showed that quinoa could be successfully cultivated in the new farming areas. Quinoa yields were higher than those reported in its place of origin, ranging from 108 kg ha-1, obtained by KU-2 in Washington State, to 9667 kg ha-1, obtained by Longli in China. Although quinoa is considered a crop with low input requirements, positive grain yield response was observed following increasing fertilization rates. Quinoa needs 2 to 4.6 kg of nitrogen to produce 1q of grain yield. In terms of phosphorus and potassium, quinoa needs 3.7 kg P2O5 and 4.3 kg K2O to produce 1 ton of total biomass. Quinoa has low water requirements (300-400 mm). However, a positive response was recorded with water quantities up to 866 mm. During our investigation, weed control in quinoa crop is still undeveloped and usually done manually. Research addressing this issue can increase quinoa yields and decrease the production cost. Downey mildew and birds’ attack are the major phytosanitary problems affecting quinoa grain yield. Other pests such as miners and aphids can also affect the health of quinoa, but their injury is not a serious problem. After the harvest, saponins found in the out layer of the seed can be removed through washing and mechanical pearling process, but the latter technic was found to be efficient and cost effective to reduce the saponin content. Our results constitute the first recommendation base for the adequate worldwide agronomic practices of quinoa crop.

Deciding when to plant is critical for smallholders in Africa. If they plant too early, farmers risk seedling death if the rains are not established; if they plant too late, there will not be enough rain to sustain the crop through critical development periods. In this study, we present a new decision support tool (DST) that accounts for the trade-off in the risks of early and late planting through advisories based on both short- and long-range forecasts of crop water availability. Unlike most existing operational systems, which are based solely on rainfall, the DST presented here uses ensemble forecasts of soil moisture to estimate the optimal planting date at a local scale. Evaluations using >30,000 observations of planting date and yield in Kenya, Rwanda, Uganda, Zambia and Malawi demonstrate that that planting at the optimal time would increase yield by 7–10% overall, and up to 20% for late planting farmers. The DST has been piloted by One Acre Fund for the 2019–2020, 2020–2021, and 2021–2022 seasons and there is strong demand for the service to be extended further. We conclude from the evaluations and pilots that the planting date DST has the potential to strengthen farmer decision making and hence their resilience to climate variability and change.

Planting date and cultivar maturity group (MG) are major management factors affecting soybean [Glycine max (L.) Merr.] yield, but their effect on seed oil and protein concentration, and in particular meal protein concentration, is less understood. We quantified changes in seed oil and protein, and estimated meal protein concentration, and total oil and protein yield in response to planting date and cultivar MG ranging from 3 to 6 and across locations comprising a 8.3° range in latitude in the U.S. Midsouth. Our results show that delayed planting date and later cultivar maturity reduced oil concentration, and this was partially associated with a decrease in temperature during the seed fill phase. Thus, optimum cultivar MG recommendations to maximize total oil yield (in kg ha-1) for planting dates in May and June required relatively earlier cultivar MGs than those recommended to maximize seed yield. For planting dates in April, short-season MG 3 cultivars did not increase oil yield compared to full-season MG 4 or 5 cultivars due to a quadratic yield response to planting date at most locations. Planting date and cultivar maturity effects on seed protein concentration were not always consistent with the effects on estimated meal protein concentration after oil extraction. Meal protein concentration decreased with lower temperatures during seed fill, and when the start of seed fill occurred after August 15, but relatively short-season cultivar MGs reduced the risk of low meal protein concentration. Meal protein concentration is a trait of interest for the feed industry that would be beneficial to report in future studies evaluating genetic, management, and environmental effects on seed protein concentration. [ABSTRACT FROM AUTHOR]

Planting date and cultivar maturity group (MG) are major management factors affecting soybean [Glycine max (L.) Merr.] yield, but their effect on seed oil and protein concentration, and in particular meal protein concentration, is less understood. We quantified changes in seed oil and protein, and estimated meal protein concentration, and total oil and protein yield in response to planting date and cultivar MG ranging from 3 to 6 and across locations comprising a 8.3° range in latitude in the U.S. Midsouth. Our results show that delayed planting date and later cultivar maturity reduced oil concentration, and this was partially associated with a decrease in temperature during the seed fill phase. Thus, optimum cultivar MG recommendations to maximize total oil yield (in kg ha–1) for planting dates in May and June required relatively earlier cultivar MGs than those recommended to maximize seed yield. For planting dates in April, short-season MG 3 cultivars did not increase oil yield compared to full-season MG 4 or 5 cultivars due to a quadratic yield response to planting date at most locations. Planting date and cultivar maturity effects on seed protein concentration were not always consistent with the effects on estimated meal protein concentration after oil extraction. Meal protein concentration decreased with lower temperatures during seed fill, and when the start of seed fill occurred after August 15, but relatively short-season cultivar MGs reduced the risk of low meal protein concentration. Meal protein concentration is a trait of interest for the feed industry that would be beneficial to report in future studies evaluating genetic, management, and environmental effects on seed protein concentration.

Fusarium wilt caused by Fusarium oxysporum f. sp. vasinfectum race 4 (FOV4) is an early season disease causing root rot, seedling wilt, and death. To develop an appropriate field evaluation method for resistance to FOV4 in cotton breeding, the objectives of this study were to investigate the effects of cultivar, planting date, and inoculum density on disease progression in 2020–2021. Results showed that the usual local mid-April planting had the lowest disease severity (DSR) or mortality rate (MR) in 2020 and 2021. DSR or MR increased at the late April and early May plantings in both years and reached the highest at the early May planting in 2020, while MR in 2021 was followed by a decrease in the late May planting and reached the highest in the mid-June planting. Local daily low temperatures between mid-April and mid-June were favorable for FOV4 infections, whereas daily high temperatures at 35°C or higher suppressed wilt severity. When seedlings at the 2-true leaf stage were inoculated with 104, 105, 106, and 107 conidia ml−1 per plant in 2020, DSR was low but a linear relationship between inoculum density and DSR was observed. When a FOV4-infested soil supplemented with artificial inoculation was used, disease progression in three moderately susceptible or moderately resistant cultivars followed a linear model, while it followed a quadratic model in the highly susceptible Pima S-7 cultivar only. Among the other three cultivars, FM 2334GLT had the lowest DSR or MR except for one planting date in both years, followed by PHY 725 RF and Pima PHY 881 RF in ascending order, which were consistent with the difference in regression coefficients of the linear models. This study demonstrates that disease progression curves due to FOV4 can be used to compare responses to FOV4 infections among cotton genotypes in cotton breeding and genetic studies, regardless of planting date and inoculation method. [ABSTRACT FROM AUTHOR]

Fusarium wilt caused by Fusarium oxysporum f. sp. vasinfectum race 4 (FOV4) is an early season disease causing root rot, seedling wilt, and death. To develop an appropriate field evaluation method for resistance to FOV4 in cotton breeding, the objectives of this study were to investigate the effects of cultivar, planting date, and inoculum density on disease progression in 2020–2021. Results showed that the usual local mid-April planting had the lowest disease severity (DSR) or mortality rate (MR) in 2020 and 2021. DSR or MR increased at the late April and early May plantings in both years and reached the highest at the early May planting in 2020, while MR in 2021 was followed by a decrease in the late May planting and reached the highest in the mid-June planting. Local daily low temperatures between mid-April and mid-June were favorable for FOV4 infections, whereas daily high temperatures at 35°C or higher suppressed wilt severity. When seedlings at the 2-true leaf stage were inoculated with 104, 105, 106, and 107 conidia ml−1 per plant in 2020, DSR was low but a linear relationship between inoculum density and DSR was observed. When a FOV4-infested soil supplemented with artificial inoculation was used, disease progression in three moderately susceptible or moderately resistant cultivars followed a linear model, while it followed a quadratic model in the highly susceptible Pima S-7 cultivar only. Among the other three cultivars, FM 2334GLT had the lowest DSR or MR except for one planting date in both years, followed by PHY 725 RF and Pima PHY 881 RF in ascending order, which were consistent with the difference in regression coefficients of the linear models. This study demonstrates that disease progression curves due to FOV4 can be used to compare responses to FOV4 infections among cotton genotypes in cotton breeding and genetic studies, regardless of planting date and inoculation method.

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future. [ABSTRACT FROM AUTHOR]

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future.

This is the first large-scale study to assess the climate change impact on the grain yield of rainfed wheat for three provinces of contrasting climatic conditions (temperate, cold semi-arid, and hot arid) in Iran. Five integrative climate change scenarios including +0.5°C temperature plus−5% precipitation, +1°C plus−10%, +1.5°C plus−15%, +2°C plus−20%, and +2.5°C plus−25% were used and evaluated. Nitrogen fertilizer and shifting planting dates were tested for their suitability as adaptive strategies for rainfed wheat against the changing climate. The climate change scenarios reduced the grain yield by −6.9 to −44.8% in the temperate province Mazandaran and by −7.3 to −54.4% in the hot arid province Khuzestan but increased it by +16.7% in the cold semi-arid province Eastern Azarbaijan. The additional application of +15, +30, +45, and +60 kg ha−1 nitrogen fertilizer as urea at sowing could not, in most cases, compensate for the grain yield reductions under the climate change scenarios. Instead, late planting dates in November, December, and January enhanced the grain yield by +6 to +70.6% in Mazandaran under all climate change scenarios and by +94 to +271% in Khuzestan under all climate change scenarios except under the scenario +2.5°C temperature plus−25% precipitation which led to a grain yield reduction of −85.5%. It is concluded that rainfed wheat production in regions with cold climates can benefit from the climate change, but it can be impaired in temperate regions and especially in vulnerable hot regions like Khuzestan. Shifting planting date can be regarded as an efficient yield-compensating and environmentally friendly adaptive strategy of rainfed wheat against the climate change in temperate and hot arid regions.

Edamame is a food-grade soybean [Glycine max (L.) Merr.] that is harvested immature between the R6 and R7 reproductive stages. To be labeled as a premium product, the edamame market demands large pod size and intense green color. A staggered harvest season is critical for the commercial industry to post-harvest process the crop in a timely manner. Currently, there is little information to assist in predicting the optimum time to harvest edamame when the pods are at their collective largest size and greenest color. The objectives of this study were to assess the impact of cultivar, planting date, and harvest date on edamame color, pod weight, and a newly minted Edamame Harvest Quality Index combining both aforementioned factors. And to predict edamame harvest quality based on phenological stages, thermal units, and planting dates. We observed that pod color and weight depended on the cultivar, planting date, and harvest date combination. Our results also indicated that edamame quality is increased with delayed planting dates and that quality was dependent on harvest date with a quadratic negative response to delaying harvest. Maximum quality depended on cultivar and planting and harvest dates, but it remained stable for an interval of 18–27 days around the peak. Finally, we observed that the number of days between R1 and harvest was consistently identified as a key factor driving edamame quality by both stepwise regression and neural network analysis. These research results will help define a planting and harvest strategy for edamame production in Arkansas and the United States Mid-South.

As resistance to herbicides limits growers' weed management options, integrated weed management (IWM) systems that combine non-chemical tactics with herbicides are becoming critical. A 2 year integrated weed management (IWM) study was conducted at three locations in VA, USA. The factorial study evaluated: (1) soybean planting date (early or late planted) (2) with or without winter cover (cereal rye/wheat or no cover), and (3) with or without harvest weed seed control (HWSC). Prior to soybean planting in the first year, winter cover resulted in a 22% reduction in common ragweed density compared to no cover. At soybean harvest in the first year, the lowest common ragweed densities were in the late planted plots following winter wheat, and common ragweed aboveground biomass was reduced by 46 and 22% at two locations in late planted compared to early planted soybean. To evaluate the impact of the first year's treatments and HWSC, full season soybeans were planted across the trial in the second year. Prior to soybean planting in the second year, late planting in the first year common ragweed density was reduced by 83% at one location, but significant reductions were not observed elsewhere. When comparing winter cover to no cover, common ragweed densities were reduced by 31 and 49% at two locations and densities were similar at the third location. Harvest weed seed control reduced common ragweed density by 43% at one location compared to the conventional harvest plots but no significant reductions were observed at the other locations or at other rating timings. However, there was a significant location by planting date by winter cover interaction and the overall lowest common ragweed densities (4.1 to 10.3 plants m−2) were in the late planted plots with winter cover. This research indicated that winter cover, late planting, and HWSC can reduce common ragweed populations with late planting being the most influential. Therefore, double-cropping soybean after wheat is likely the most viable means to better control common ragweed using IWM as it combines both winter cover and late planting date.

Edamame is a food-grade soybean [ Glycine max (L.) Merr.] that is harvested immature between the R6 and R7 reproductive stages. To be labeled as a premium product, the edamame market demands large pod size and intense green color. A staggered harvest season is critical for the commercial industry to post-harvest process the crop in a timely manner. Currently, there is little information to assist in predicting the optimum time to harvest edamame when the pods are at their collective largest size and greenest color. The objectives of this study were to assess the impact of cultivar, planting date, and harvest date on edamame color, pod weight, and a newly minted Edamame Harvest Quality Index combining both aforementioned factors. And to predict edamame harvest quality based on phenological stages, thermal units, and planting dates. We observed that pod color and weight depended on the cultivar, planting date, and harvest date combination. Our results also indicated that edamame quality is increased with delayed planting dates and that quality was dependent on harvest date with a quadratic negative response to delaying harvest. Maximum quality depended on cultivar and planting and harvest dates, but it remained stable for an interval of 18–27 days around the peak. Finally, we observed that the number of days between R1 and harvest was consistently identified as a key factor driving edamame quality by both stepwise regression and neural network analysis. These research results will help define a planting and harvest strategy for edamame production in Arkansas and the United States Mid-South. [ABSTRACT FROM AUTHOR]

Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.

Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios. [ABSTRACT FROM AUTHOR]

Climate changes are affecting the plant production, including sugar beet growing especially in the southern and central parts of the Europe. Modifying the sowing and harvesting dates are one of the most often used adaptations in sugar beet cultivation. The aim of this study was to assess the interactions between planting date and sugar beet genotypes for different harvest dates with recommendation for duration of vegetation period for specific hybrids in order to achieve the best performance and to evaluate influence of climatic factors on sugar yield. Three-way analysis of variance and AMMI (Additive main effect and multiple interactions) analysis were performed to investigate interaction between main factors. Analysis of variance revealed that genotypes (G), planting date (PD), harvest date (HD) and interaction G × PD significantly affected sugar yield in 2016. In 2017 genotypes, planting date, harvest date and G x PD interaction significantly affected sugar yield on probability level of 1%, while PD × HD interaction had significant effect on probability level of 5%. Results of AMMI analysis enabled discrimination of genotypes with the highest level of stability in certain planting dates. Hybrids with combined yield and sugar content (NZ type) should have the advantage in earlier planting dates compared to of sugar beet hybrids with higher sugar content (Z type). However, in shortened vegetation period Z type hybrids are more stable and with better sugar yield results. Results of our study suggest that delaying the harvest date decreases differences between sugar yields obtained from hybrids sown in different planting dates. Major factors in the study affecting sugar yield were growing degree days, insolation and number of days from planting to harvest.

For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011–2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89–106 day of the year (DOY) for the 30–35°N, 107–118 DOY for the 35–40°N,

Climate changes are affecting the plant production, including sugar beet growing especially in the southern and central parts of the Europe. Modifying the sowing and harvesting dates are one of the most often used adaptations in sugar beet cultivation. The aim of this study was to assess the interactions between planting date and sugar beet genotypes for different harvest dates with recommendation for duration of vegetation period for specific hybrids in order to achieve the best performance and to evaluate influence of climatic factors on sugar yield. Three-way analysis of variance and AMMI (Additive main effect and multiple interactions) analysis were performed to investigate interaction between main factors. Analysis of variance revealed that genotypes (G), planting date (PD), harvest date (HD) and interaction G × PD significantly affected sugar yield in 2016. In 2017 genotypes, planting date, harvest date and G x PD interaction significantly affected sugar yield on probability level of 1%, while PD × HD interaction had significant effect on probability level of 5%. Results of AMMI analysis enabled discrimination of genotypes with the highest level of stability in certain planting dates. Hybrids with combined yield and sugar content (NZ type) should have the advantage in earlier planting dates compared to of sugar beet hybrids with higher sugar content (Z type). However, in shortened vegetation period Z type hybrids are more stable and with better sugar yield results. Results of our study suggest that delaying the harvest date decreases differences between sugar yields obtained from hybrids sown in different planting dates. Major factors in the study affecting sugar yield were growing degree days, insolation and number of days from planting to harvest. [ABSTRACT FROM AUTHOR]

For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011–2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89–106 day of the year (DOY) for the 30–35°N, 107–118 DOY for the 35–40°N, <119 DOY for 40–45°N, and <129 DOY for 45–50°N, and (iii) both, yield contest and literature datasets portrayed that planting date becomes a more relevant factor when planting late, presenting a relatively smaller planting window in high-compared to low-latitudes. [ABSTRACT FROM AUTHOR]

Field trials were carried out in the Sudan Savannah of Nigeria to assess the usefulness of CERES–maize crop model as a decision support tool for optimizing maize production through manipulation of plant dates. The calibration experiments comprised of 20 maize varieties planted during the dry and rainy seasons of 2014 and 2015 at Bayero University Kano and Audu Bako College of Agriculture Dambatta. The trials for model evaluation were conducted in 16 different farmer fields across the Sudan (Bunkure and Garun—Mallam) and Northern Guinea (Tudun-Wada and Lere) Savannas using two of the calibrated varieties under four different sowing dates. The model accurately predicted grain yield, harvest index, and biomass of both varieties with low RMSE-values (below 5% of mean), high d-index (above 0.8), and high r-square (above 0.9) for the calibration trials. The time series data (tops weight, stem and leaf dry weights) were also predicted with high accuracy (% RMSEn above 70%, d-index above 0.88). Similar results were also observed for the evaluation trials, where all variables were simulated with high accuracies. Estimation efficiencies (EF)-values above 0.8 were observed for all the evaluation parameters. Seasonal and sensitivity analyses on Typic Plinthiustalfs and Plinthic Kanhaplustults in the Sudan and Northern Guinea Savannas were conducted. Results showed that planting extra early maize varieties in late July and early maize in mid-June leads to production of highest grain yields in the Sudan Savanna. In the Northern Guinea Savanna planting extra-early maize in mid-July and early maize in late July produced the highest grain yields. Delaying planting in both Agro-ecologies until mid-August leads to lower yields. Delaying planting to mid-August led to grain yield reduction of 39.2% for extra early maize and 74.4% for early maize in the Sudan Savanna. In the Northern Guinea Savanna however, delaying planting to mid-August resulted in yield reduction of 66.9 and 94.3% for extra-early and early maize, respectively.

In Brassica napus, floral development is a decisive factor in silique formation, and it is influenced by many cultivation practices including planting date. However, the effect of planting date on floral initiation in canola is poorly understood at present. A field experiment was conducted using a split plot design, in which three planting dates (early, 15 Sep, middle, 1 Oct, and late, 15 Oct) served as main plot and five varieties differing in maturity (1358, J22, Zhongshuang 11, Zheshuang 8, and Zheyou 50) employed as subplot. The purpose of this study was to shed light on the process of floral meristem (FM) differentiation, the influence of planting date on growth period (GP) and floral initiation, and silique formation. The main stages of FM developments can be divided into four stages: first, the transition from shoot apical meristem to FM; second, flower initiation; third, gynoecium and androecium differentiation; and fourth, bud formation. Our results showed that all genotypes had increased GPs from sowing to FM differentiation as planting date was delayed while the GPs from FM differentiation to budding varied year by year except the very early variety, 1358. Based on the number of flowers present at the different reproductive stages, the flowers produced from FM differentiation to budding closely approximated the final silique even though the FM differentiated continuously after budding and peaked generally at the middle flowering stage. The ratio of siliques to maximum flower number ranged from 48% to 80%. These results suggest that (1) the period from FM differentiation to budding is vital for effective flower and silique formation although there was no significant correlation between the length of the period and effective flowers and siliques, and (2) the increased number of flowers from budding were generally ineffective. Therefore, maximizing flower numbers prior to budding will improve silique numbers, and reducing FM degeneration should also increase final silique formation. From the results of our study, we offer guidelines for planting canola varieties that differ in maturity in order to maximize effective flower numbers.

Information on the effects of management practices on soybean seed composition is scarce. Therefore, the objective of this research was to investigate the effects of planting date (PD) and seeding rate (SR) on seed composition (protein, oil, fatty acids, and sugars) and seed minerals (B, P, and Fe) in soybean grown in two row-types (RT) on the Mississippi Delta region of the Midsouth USA. Two field experiments were conducted in 2009 and 2010 on Sharkey clay and Beulah fine sandy loam soil at Stoneville, MS, under irrigated conditions. Soybean were grown in 102 cm single-rows and 25 cm twin-rows in 102 cm centers at seeding rates of 20, 30, 40, and 50 seeds m–2. The results showed that in May and June planting, protein, glucose, P, and B concentrations increased with increased SR, but at the highest seeding rates (40 and 50 seeds m–2), the concentrations remained constant or declined. Palmitic, stearic, and linoleic acid concentrations were the least responsive to SR increases. Early planting resulted in higher oil, oleic acid, sucrose, B, and P on both single and twin-rows. Late planting resulted in higher protein and linolenic acid, but lower oleic acid and oil concentrations. The changes in seed constituents could be due to changes in environmental factors (drought and temperature), and nutrient accumulation in seeds and leaves. The increase of stachyose sugar in 2010 may be due to a drier year and high temperature in 2010 compared to 2009; suggesting the possible role of stachyose as an environmental stress compound. Our research demonstrated that PD, SR, and RT altered some seed constituents, but the level of alteration in each year dependent on environmental factors such as drought and temperature. This information benefits growers and breeders for considering agronomic practices to select for soybean seed nutritional qualities under drought and high heat conditions.

Information on the effects of management practices on soybean seed composition is scarce. Therefore, the objective of this research was to investigate the effects of planting date (PD) and seeding rate (SR) on seed composition (protein, oil, fatty acids, and sugars) and seed minerals (B, P, and Fe) in soybean grown in two row-types (RTs) on the Mississippi Delta region of the Midsouth USA. Two field experiments were conducted in 2009 and 2010 on Sharkey clay and Beulah fine sandy loam soil at Stoneville, MS, USA, under irrigated conditions. Soybean were grown in 102 cm single-rows and 25 cm twin-rows in 102 cm centers at SRs of 20, 30, 40, and 50 seeds m-2. The results showed that in May and June planting, protein, glucose, P, and B concentrations increased with increased SR, but at the highest SRs (40 and 50 seeds m-2), the concentrations remained constant or declined. Palmitic, stearic, and linoleic acid concentrations were the least responsive to SR increases. Early planting resulted in higher oil, oleic acid, sucrose, B, and P on both single and twin-rows. Late planting resulted in higher protein and linolenic acid, but lower oleic acid and oil concentrations. The changes in seed constituents could be due to changes in environmental factors (drought and temperature), and nutrient accumulation in seeds and leaves. The increase of stachyose sugar in 2010 may be due to a drier year and high temperature in 2010 compared to 2009; suggesting the possible role of stachyose as an environmental stress compound. Our research demonstrated that PD, SR, and RT altered some seed constituents, but the level of alteration in each year dependent on environmental factors such as drought and temperature. This information benefits growers and breeders for considering agronomic practices to select for soybean seed nutritional qualities under drought and high heat conditions. [ABSTRACT FROM AUTHOR]