Exploring the seasonal yield variability, production risk and efficiency: the case of rice farms in Bangladesh

Farmers in developing countries face different rice production risks, influencing yield variability. This study investigates the technical efficiency (TE) and production risk of Boro and Aman rice in Bangladesh. A stochastic production frontier has been used assuming a true random effect (TRE) model with flexible risk properties using two-year seasonal plot-level panel data of 5088 observations for Boro rice and 5638 observation for Aman rice. The empirical result of the risk model shows that labor, fertilizer, seed, and farm capital have a significant risk-decreasing effect for Boro rice. In contrast, the cultivated area of rice and average mean temperature have a significant risk-increasing effect on Boro rice. Labor and pesticide have significant risk-decreasing effects, whereas cultivated area, fertilizer and seed has a significant risk-increasing effect on Aman rice production. The average TE was 76% and 72% for Boro and Aman rice, respectively. Results suggest a high degree of variability in TE estimates, and the average farmer could increase rice yield by 24% and 28% by improving technical management without increasing the existing inputs. Large farms are more technically efficient than other farm categories. It is also observed that small farms and medium farms significantly decrease technical efficiency for Boro rice while significantly increasing for Aman rice. Moreover, technical efficiency declined over time for Boro rice, while it improved for Aman rice in Bangladesh. Production risk, however, declined over time for both Boro and Aman rice.

Impacts of climate change on yield of cereal crops in northern climatic region of Pakistan

This study investigates the impacts of climate change on yield of selected cereal crops (wheat and maize) in the northern climatic region of Khyber Pakhtunkhwa (KP) province of Pakistan for the period 1986-2015. The first-generation unit root tests such as the Levin, Lin, and Chu (LLC), augmented Dickey-Fuller (ADF)-Fisher, and the second-generation unit root tests such as cross-sectional augmented Im-Pesaran-Shin (CIPS) and cross-sectional ADF (CADF) are used to check stationarity of the series. The cointegration among the variables is discovered via Pedroni test and Westerlund method. The long- and short-run impacts of climatic variables (average precipitation, maximum temperature, and minimum temperature) on yield of wheat and maize crops are assessed through the autoregressive distributed lag (ARDL) model. The empirical findings reveal that average precipitation has a significantly positive impact on yield of both crops in long- as well as short-run. The results further reveal that the effect of average minimum temperature on both crops is insignificant in long-run. However, the short-run effect of average minimum temperature is significantly positive on yield of maize crop but insignificant on yield of wheat crop. In long-run, an increase in average maximum temperature negatively affects crop yield. In short-run, however, it positively affects the yield of wheat and maize crops. The study recommends that increase in area under cultivation, development of advanced irrigation system, and farmers' access to metrological information will help in lowering the drastic impacts of climate change on crop productivity.

Implications of intra-plot heterogeneity for yield estimation accuracy: Evidence from smallholder maize systems in Ethiopia

Intra-plot heterogeneity in yield is often observed in smallholder farming systems, although its implications for yield measurement remain under-investigated. Using a unique dataset on smallholder maize production in Ethiopia, we quantify the magnitude of inter- and intra-plot heterogeneity, describe the relationship between intra-plot heterogeneity and maize productivity, and document the implications of intra-field heterogeneity on the accuracy of alternative yield estimation protocols. Our data include five common yield estimation protocols, as well as full plot harvests of 230 smallholder maize fields. We surveyed agronomic decisions, biophysical variables, and accessibility characteristics of the surveyed fields. We quantify intra-plot heterogeneity using the coefficient of variation (CV) of stand density, cob weight, and maize grain yield. A generalized linear mixed model is used to explore the relationship between these variables and the method- and heterogeneity-dependence of yield estimation accuracy. We find inter-plot CV values ranging from 32 to 56 %, 22 to 73 % and 39 to 49 % in population density, cob weight and grain yield, respectively. Intra-plot heterogeneity constituted most of this variation, with across-method mean CV values of 41 %, 82 % and 63 %, respectively, of the total variability in population density, cob weight and grain yield. A rise in intra-plot heterogeneity of 0.5 % to 0.8 % is associated with a significant increase in yield estimation error under alternative yield estimation protocols. Regression analysis shows that interactions in agronomic decisions, input intensity and plot accessibility factors dictate intra-plot heterogeneity and method accuracy in smallholder systems. Intra-plot heterogeneity is larger than inter-plot heterogeneity in the current study area. Our analysis shows that the effect of intra-plot heterogeneity on yield estimation accuracies is method-dependent and yield estimation methods that fail to capture true intra-plot heterogeneity are more error-prone. Results of such estimations should be considered with caution when used as the basis of decision-making.

Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data

High quality of long-term daily weather data is essential for simulating crop production and its variability. However, daily weather data with adequate duration and required quality are not available in many regions. This study has evaluated the suitability of AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) weather data for simulating rainfed wheat evapotranspiration (ET_c) and yield. Daily AgMERRA were compared with corresponding observed weather data of 11 land stations across the northeast Iran, considering the different periods from 1980 to 2010. Cropwat and CSM-CERES-Wheat models were used to simulate ET_c and yield of rainfed wheat, respectively. The comparison of daily AgMERRA with observations resulted in the highest correlation (r² > 70%) and good agreement (d > 0.77 and NRMSE < 30%) between climate variables, except for daily wind speed and precipitation at all locations. However, when daily precipitation data were aggregated into 15-day periods, agreement and correlation improved. According to the monthly comparison, the largest bias between AgMERRA temperature and radiation with land observations was obtained from June to August (summer season). Results also indicated that the distribution of simulated ET_c and yield using AgMERRA was within 10% of the simulated yield using observations at 73% and 100% of locations, respectively. The degree of variation of AgMERRA-simulated ET_c and yield was very similar to the calculated coefficient of variation in simulated ET_c and yield based on observations at 73% of locations. However, simulation of ET_c and yield using AgMERRA for single years was more uncertain when compared with simulated ET_c and yield based on observations for the same year. It is concluded that AgMERRA can provide a robust estimate of long-term average ET_c and yield of wheat than the ET_c and yield of a single year in regions that there is no long-term weather data available.

Wheat yield response to input and socioeconomic factors under changing climate: Evidence from rainfed environments of Pakistan

More than three-quarters of the world's total cultivated land is under rainfed farming, producing almost 70% of total food. Most food production comes from developing and least favored nations. Pakistan, a developing country with an agro-based economy, is facing severe threats from climate change. Rainfed agriculture, especially wheat farming, is highly susceptible due to its heavy dependency on precipitation, one of the most important climatic parameters. Wheat is the main food crop, as well as a major source of calorific intake, for millions of people in Pakistan. This study aims to quantify the impacts of climate variability on mean yield levels and yield variability of wheat crop in the rainfed zone of Pakistan. Multistage random sampling technique is used for primary data collection from 400 rainfed wheat farmers during the 2016-17 crop season. The study uses primary data on crop input-output, management, socioeconomic, institutional, and historical climatic data (1980-2017). The data are analyzed employing the Just and Pope (J-P) stochastic production function approach with linear and non-linear functional forms. The results reveal that temperature rise negatively affects observed wheat mean yields, while cumulative precipitation positively affected it. Further, input market access, seed rate, and cumulative precipitation also cause variability in yield levels, leading to yield instability. Further, farm elevation influences wheat mean yield positively while input market access influences it negatively. The findings of the study have important implications for climate resilient wheat farming. Timely and tailor-made adaptations need to be undertaken in the rainfed wheat farming systems of Pakistan. Creating awareness among farmers about the optimal use of agronomic inputs under changing climatic conditions could be an effective adaptation strategy that improves yields and copes with yield instability.

Grain legume yields are as stable as other spring crops in long-term experiments across northern Europe

Grain legumes produce high-quality protein for food and feed, and potentially contribute to sustainable cropping systems, but they are grown on only 1.5% of European arable land. Low temporal yield stability is one of the reasons held responsible for the low proportion of grain legumes, without sufficient quantitative evidence. The objective of this study was to compare the yield stability of grain legumes with other crop species in a northern European context and accounting for the effects of scale in the analysis and the data. To avoid aggregation biases in the yield data, we used data from long-term field experiments. The experiments included grain legumes (lupin, field pea, and faba bean), other broad-leaved crops, spring, and winter cereals. Experiments were conducted in the UK, Sweden, and Germany. To compare yield stability between grain legumes and other crops, we used a scale-adjusted yield stability indicator that accounts for the yield differences between crops following Taylor's Power Law. Here, we show that temporal yield instability of grain legumes (30%) was higher than that of autumn-sown cereals (19%), but lower than that of other spring-sown broad-leaved crops (35%), and only slightly greater than spring-sown cereals (27%). With the scale-adjusted yield stability indicator, we estimated 21% higher yield stability for grain legumes compared to a standard stability measure. These novel findings demonstrate that grain legume yields are as reliable as those of other spring-sown crops in major production systems of northern Europe, which could influence the current negative perception on grain legume cultivation. Initiatives are still needed to improve the crops agronomy to provide higher and more stable yields in future.

Additive yield response of chickpea (Cicer arietinum L.) to rhizobium inoculation and phosphorus fertilizer across smallholder farms in Ethiopia

The impacts of rhizobium inoculation on growth and yield of chickpea have mainly been tested in experiments conducted in greenhouses or on research stations. We report the response of the crop to inoculation (I) and phosphorus fertilizer (P) application across a large number of smallholder's farms over four regions of Ethiopia, covering diverse soil fertility and agro-ecological conditions. Increased grain yields due to the soil fertility treatments was evident for 99% target farmers. On average, I and P increased grain yield by 21% and 25% respectively, while the combined application of I and P resulted in a 38% increase. However, observed grain yields on control plots and responses to the treatments on individual farms varied greatly, and relative yield responses (%; yield of P and/I minus control yield, divided by control yield) ranged from 3% to 138%. With the exception of a few extremely poorly yielding locations, average responses to P and I were high across a wide range of control yields, indicating the possibility of boosting chickpea productivity for smallholders with P fertilizer and inoculant technology. Variation in response to rhizobium inoculation was mostly independent of agro-ecology and soil type although it was found to be low on a number of farms with extremely high N contents (%). Assuming that a relative yield increase of 10% due to treatment effects is required to be visible, 71%, 73% and 92% of the farmers observed a yield benefit by applying P, I, and P + I, respectively. The results are discussed with respect to the additive benefits of P fertilizers and rhizobial inoculation and their implications for wide scale promotion of inoculant technology to smallholders.

Understanding variability in the benefits of N2-fixation in soybean-maize rotations on smallholder farmers' fields in Malawi

Soybean production can contribute to the nitrogen economy of smallholder farming systems, but our understanding of factors explaining variability in nitrogen fixation and rotational benefits across farms and regions is limited. Biological nitrogen fixation (BNF) was quantified with the natural abundance method in 150 farmer-managed soybean plots under different varieties and inputs in Dowa, Mchinji and Salima districts of Malawi. Soybean yielded on average 1.2 t ha^-1 grain and the above-ground biomass at mid pod filling (R5.5) was 2.8 t ha^-1 and contained in total 63 kg ha^-1 nitrogen derived from the atmosphere (Ndfa). Locally sourced varieties obtained a larger %Ndfa (65%) than the 'improved' variety Nasoko (53%). The %Ndfa was positively associated with soil sand content, sowing date, plant population and biomass accumulation, but it was not affected by inoculation with rhizobia or the combination of inoculation and NPK fertiliser application. Quantities of N₂ fixed differed between regions and years, and was enhanced by applying inoculant and fertiliser together, leading to more biomass accumulation and larger grain yields. Soil available P and exchangeable K contents also increased the total amount of N₂ fixed. In a related trial, continuous maize yields were compared with maize following soybean in 53 farmer-managed fields. Average yield in continuous maize was 2.5 t ha^-1, while maize after soybean produced 3.5 t ha^-1 (139% of continuous maize). Farmers with higher maize yields, who applied external nutrient inputs, and with a larger value of household assets achieved greater yield responses to rotation with soybean. A relative yield increase of more than 10% was observed on 59, 90 and 77% of the fields in Dowa, Mchinji and Salima respectively. We conclude that fields of soybean and maize that receive adequate nutrient inputs and good management to ensure good yields benefit most in terms of quantities of N₂ fixed by the legume and the yield response of the following maize crop. The results suggest that the promotion of soybean-maize rotations should be done through an integrated approach including the promotion of appropriate soil and crop management techniques. Furthermore, they suggest that wealthier households are more likely to apply adequate nutrient inputs and good crop management practices and are likely to receive larger maize yield responses to the incorporation of soybean.

Agricultural vulnerability over the Chinese Loess Plateau in response to climate change: Exposure, sensitivity, and adaptive capacity

Understanding how the vulnerability of agricultural production to climate change can differ spatially has practical significance to sustainable management of agricultural systems worldwide. Accordingly, this study developed a conceptual framework to assess the agricultural vulnerability of 243 rural counties on the Chinese Loess Plateau. Indicators representing the climate/agriculture interface were selected to describe exposure and sensitivity, while stocks of certain capitals were used to describe adaptive capacity. A vulnerability index for each county was calculated and the spatial distribution was mapped. Results showed that exposure, sensitivity, and adaptive capacity occur independently, with most contributing indicator values concentrated in a narrow range after normalization. Within the 49 most vulnerable counties, which together encompass 81 % of the vulnerability index range, 42 were characterized by high exposure and sensitivity but low adaptive capacity. The most vulnerable area was found to be located in the central northeast-southwest belt of Loess Plateau. Adaptation measures for both ecological restoration and economic development are needed and potential adaptation options need further investigation.

Insect pollination reduces yield loss following heat stress in faba bean (Vicia faba L.)

Global food security, particularly crop fertilization and yield production, is threatened by heat waves that are projected to increase in frequency and magnitude with climate change. Effects of heat stress on the fertilization of insect-pollinated plants are not well understood, but experiments conducted primarily in self-pollinated crops, such as wheat, show that transfer of fertile pollen may recover yield following stress. We hypothesized that in the partially pollinator-dependent crop, faba bean (Vicia faba L.), insect pollination would elicit similar yield recovery following heat stress. We exposed potted faba bean plants to heat stress for 5 days during floral development and anthesis. Temperature treatments were representative of heat waves projected in the UK for the period 2021-2050 and onwards. Following temperature treatments, plants were distributed in flight cages and either pollinated by domesticated Bombus terrestris colonies or received no insect pollination. Yield loss due to heat stress at 30 °C was greater in plants excluded from pollinators (15%) compared to those with bumblebee pollination (2.5%). Thus, the pollinator dependency of faba bean yield was 16% at control temperatures (18-26 °C) and extreme stress (34 °C), but was 53% following intermediate heat stress at 30 °C. These findings provide the first evidence that the pollinator dependency of crops can be modified by heat stress, and suggest that insect pollination may become more important in crop production as the probability of heat waves increases.